Screening, diagnosis and prognosis of autism and other developmental disorders

ABSTRACT

The invention provides a method and system combining functional genomic and genetic, proteomic, anatomic neuroimaging, functional neuroimaging, behavioral and clinical measurements and data analyses for autism pediatric population screening, diagnosis or prognosis. More specifically, the invention provides a weighted gene and feature test for autism which uses a weighted gene signature matrix for comparison to a reference database of healthy and afflicted individuals. The invention also provides normalized gene expression value signatures for comparison to a reference database. The invention additionally combines either the weighted gene or the normalized gene analysis with comparisons to a gene-networks signature matrix, a multi-modal signature matrix, and a collateral features signature matrix for improved accuracy in screening, diagnostic and prognostic relevance for autism, particularly for newborns, babies ages birth to 1 year, toddlers ages 1 to 2 years, toddlers ages 2 to 3 years and young children ages 3 through 4 years.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of PCT Application No. PCT/US2013/052094 filed Jul. 25, 2013, which claims priority to U.S. Provisional Application No. 61/675,928, filed Jul. 26, 2012, the entire contents of which are incorporated by reference herewith.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under grant Nos. P50-MH081755, R01-MH080134, and R01-MH036840 awarded by National Institute of Mental Health (NIMH). The government has certain rights in the invention.

FIELD OF THE INVENTION

The invention relates generally to screening, diagnosis and prognosis of autism and other development disorders. More specifically, the invention relates to the use of a combination of functional genomic signatures and multimodality signatures in screening for autism risk and in autism diagnostics and prognostics. Its prognostics use includes prediction and characterization of likely clinical, neural and treatment progress and outcome.

BACKGROUND OF THE INVENTION

It is of the greatest importance to improve early screening and detection of risk for autism, a genetically complex neural developmental disorder affecting higher order functions such as social, communication, language and cognition. Among the benefits of early detection is that accelerating the pace of identification and treatment by even a year¹ can have a considerable impact on the outcome of affected newborns, infants, toddlers and young children.

Despite recent university-based research advances in the development of potential methods for screening, detection and diagnostic evaluation for autism within the first 2 years of life, the clinical translation of these methods into widespread and effective community practice in the US has not occurred. Instead, 3 to 5 years of age continues to be the age of first clinical identification and referral for treatment services for autism in much of the US¹. Studies find that on average, a child with autism is diagnostically evaluated by 4 to 5 different professionals before a final diagnosis is determined and this process can take several years during which the child does not receive suitable treatment. From a neurobiological perspective, this is particularly problematic given that functional connections in the brain are strongly established during the first few years of life^(2, 3). Starting treatment after many neural connections have already been formed (rather than before) will likely reduce treatment efficacy and impact. Hundreds of websites, articles, blogs and government, professional and private organizations cite the need for the early screening, detection, diagnosis and treatment referral for children with autism, yet the gulf separating university-based research advances in early detection and actual community clinical practice is alarming; For example, in 2012 the CDC documented the median age of autism identification in the US (based on 2008 data) is about 4 years¹. The median age of treatment referral is correspondingly even later in the US. Further, there remain large underserved segments of the population, both in terms of early screening and access to empirically-validated early intervention. The magnitude of the problem is staggering: Given recent prevalence estimates and the U.S. birth rate, every year 52,000 to 84,000 infants will go on to develop autism. Thus, there is an enormous and urgent need for useful and cost-effective pediatric population screening strategies in ordinary community settings throughout the U.S. Presently, unfortunately, hundreds of thousands of toddlers and young children with autism in the U.S. are overlooked, under-treated and may have a poorer outcome than need be.

Moreover, once children are identified with having an autism spectrum disorder (ASD), science has not yet offered insight into prognosis. Will the child face consistent extreme barriers in speech, language and social development, or will he or she fall into the minority of ASD individuals that enjoy success in school and beyond. Presently, however, there are no prognostic biomarkers of autism; specifically there is a lack of prognostic biomarkers that predict and characterize likely clinical, neural and treatment progress and outcome.

Despite the importance, the high priority of discovery of risk behavioral or biological markers with clinical impact remains largely unfulfilled. Neither biological nor behavioral markers have emerged that fulfill this need in clinical settings for the general pediatric population. For example, commonly used parent report screens (e.g., Modified Checklist for Autism in Toddlers (M-CHAT), Communication and Symbolic Behavior Scales (CSBS) have valuable strengths, but also weaknesses⁴⁻⁶, including very high false positive rates. The M-CHAT has very low specificity (27%⁵) and positive predictive value (PPV, 11%) when used in the general population⁷, rendering it of limited utility in routine clinical practice. Similarly, the newest and largest study to test the efficacy of the M-CHAT conducted by Chlebowski, Robins, Barton & Fein published in 2013 found an 80% false positive rate when the tool was used alone⁸. Although high-risk baby sib studies by Zwaigenbaum⁹, Ozonoff¹⁰, Paul¹¹, Landa¹² and others have revealed key early deficits such as abnormalities in social attention⁹, they report data only at the group level and have not reported validation statistics such as PPV that are a necessary first step for determining the utility of a behavioral trait as an early marker.

Several groups have used eye tracking and reported reduced preference for biological motion²³, fixation to the eye region²⁴, head region²⁵ and difficulties in joint attention²⁶ as well as scene monitoring during explicit dyadic cues²⁷ in ASD relative to typically developing (TD) toddlers. While collectively these studies point to early developmental origins of social dysfunction, reported effects are subtle and results are provided only at the overall group level and have very weak power to detect or diagnose ASD. For example, in one study differences in fixation towards the face and eye region were no different between ASD and TD toddlers when toddlers watched a woman make a sandwich and only became evident during a specific 3-second dyadic bid condition²⁷. Moreover, validation statistics that are needed to translate eye tracking into a screening tool, such as specificity or positive predictive value, are not provided in most eye tracking studies of ASD toddlers.

While great strides have been made in understanding possible genetic risk factors¹³⁻¹⁵ and neural bases¹⁶⁻¹⁸ of autism, neither gene nor brain abnormalities published to date have translated into practical clinical population screens or tests of risk for autism in toddlers. Also, links between genetic and neural developmental abnormalities at young ages have remained largely unknown. Overall, research on potential genetic and neuroimaging biomarkers has remained largely “in the lab.”

Discovery by one of the present inventors¹⁹ that a substantial percentage of autism infants and toddlers display early brain overgrowth indicates that autism might involve abnormalities in mechanisms that regulate cell production or natural apoptosis in early life. The inventor analyzed dysregulation of genetic mechanism in autism in two ways. First, the total number of neurons in prefrontal cortex tissue in postmortem autistic boys was counted to reveal a huge 67% excess of neurons¹⁸. Second, evidence shows that dysregulation of genetic mechanism that govern neuron number in prefrontal cortex brain tissue in postmortem autistic boys¹⁴.

These discoveries have advanced the general understanding of the neural and genetic bases of ASD but not the early screening of ASD risk, diagnostic evaluation, and prognostic assessment of autism at the level of the individual child in the general pediatric population. While other studies raise the hope that MRI neuro-imaging biomarkers might be identified for use with older children or adults already known to have autism, they have not demonstrated the ability to improve risk assessment at very young ages in the general pediatric population when they are most needed. Still other studies suffer from limitations such as being based only on data from multiplex ASD families^(18,19) leaving unaddressed the majority of autistic infants in the general population, or based on algorithms that identify genes with little or no demonstrated relevance to the underlying brain maldevelopment in autism^(20,21).

Broadly speaking, “biomarkers” to date (e.g., genetic, molecular, imaging) have poor diagnostic accuracy, specificity and/or sensitivity; none have clinical outcome prognostic power; most are expensive; none are suitable as an early screening tool in community populations; and few have undergone serious clinical scrutiny and rigorous validation. For example, genetic findings have been generally non-specific, and the best characterized CNVs can occur in schizophrenia, bipolar, intellectual disability as well as ASD (e.g., 16p11.2). Few gene mutations are recurrent²². CNVs and recurrent genes combined account for a very small, arguably about 5-10%, of all ASD individuals. Thus, current DNA tests detect only rare autism cases and lack specificity. Moreover, genetic tests released by several companies detect only a small percent (5% to 20%) of ASD individuals, generally lack good specificity (because CNV, gene mutation and SNP markers in these tests are also found in a wide variety of non-ASD disorders such as schizophrenia or bipolar as well as in non-symptomatic, “typical” individuals), miss the vast majority of ASD individuals and are very expensive and out of the reach of most individuals. A genetic test targeting baby sibs of older ASD children provides only estimates of risk from less to more, but of course, parents who already have a child with ASD already know subsequent offspring are at risk. The benefit from this test is arguably small and of little practical clinical utility. No genetic finding has been shown to have clinical outcome prognostic power; that is, genetic testing does not provide information about likely later language, social or general functional progress and ability. A recent MRI “biomarker” works on adults with ASD, but diagnosis of ASD in adults is of very limited clinical value. A diffusion tensor imaging (DTI) study of small samples of infant siblings of older ASD children shows group differences too small to hold diagnostic promise. A gene expression classifier of previously diagnosed ASD 5 to 11 year olds performed in a validation set with accuracy, sensitivity and specificity at only 67.7%, 69.2% and 65.9%, respectively²¹. A metabolomics classifier tested only a sample of 4 to 6.9 year old children previously diagnosed as ASD and did not test newborns or 0 up to 4 year olds.³²

In sum, no currently reported biomarker holds promise as a primary or secondary early developmental screen or an early diagnostic or prognostic tool in ordinary community pediatric settings at young ages from birth through early childhood when these clinical tools are most needed. There are no preclinical screens or tests for risk of developing ASD with the sensitivity and specificity for routine value in clinical application. Current expectations are that ASD is so etiologically and clinically heterogeneous that no diagnostic biomarker and/or combination of behavioral or biological markers is likely to do better that detect a small percentage of cases, and that such biomarkers and/or combination of behavioral and biological markers will be either sensitive but non-specific or specific but for a tiny portion of the ASD spectrum.

SUMMARY OF THE INVENTION

The invention provides a leap beyond all current early screening, diagnostic and prognostic biomarker tests for ASD. In certain embodiments, the invention is unique because, among other advantages provided, it is the only approach utilizing multimodality (functional genomic, genetic, proteomic, anatomic neuroimaging, functional neuroimaging, and neurobehavioral) data combined with deep clinical phenotyping data all from the same individual infants and toddlers representative of the general community pediatric population. Using complex bioinformatics methods in novel ways, the invention provides novel single and multimodality signatures of ASD.

In certain embodiments, the invention is unique in the identification of genes, and gene-to-gene interactions (e.g., gene pathways, gene networks, and hub-gene activity patterns and organization including quantifiable signature features) in combination with clinical, neuroimaging and behavioral information that have high accuracy, specificity and sensitivity for early screening, diagnostic evaluation and prognostic assessment for autism of subjects including particularly those at ages from birth to 1 year, 1 year to 2 years, 2 years to 3 years, and 3 years to 4 years, and older.

The invention provides highly surprising advantages for multiple reasons: ASD is thought to be highly etiologically and clinically heterogeneous, and yet the invention in certain embodiments can accurately detect the great majority (such as at least 82%) of cases, not just a small percentage of cases (which is the best other ASD risk current biological and behavioral tests can do). There is no proven preclinical marker of ASD, and yet the invention can detect ASD with high accuracy, sensitivity and specificity before clinical symptom onset in the general natural pediatric population (not just in cases already suspected of being at high risk because of an older sibling with ASD, dysmorphology, seizure, etc). By comparison, existent genetic tests have low specificity as well as poor sensitivity, detecting only 5% to 20% of ASD cases when tested in general preclinical pediatric populations. Claims of prior art are exaggerated because they are based on tests performed on patients already highly suspected of being ASD because of prior clinical testing. The invention has surprisingly high accuracy, sensitivity and specificity in the natural pediatric setting where early screening is a major unfilled need. No prior art has discovered how to utilize clinical and neurobehavior information to differentially adjust genomic signatures so that they are tuned for the different uses in general population screening, diagnostic evaluation and prognostic assessment.

In certain embodiments, for screening, weighted gene expression patterns can be used alone or in combination with readily available standard clinical measures (head circumference, age, CSBS scores, and GeoPrefernce test score) and do not depend on neuroimaging or other tools unsuited to general population screening, while for that diagnostic or prognostic use after a child has become suspected of being at risk, weighted gene expression patterns can be used in combination with specialty tools such as MRI or fMRI to optimize diagnostic and prognostic judgments. No prior screening, diagnostic and prognostic prior art using biological measures is able to accurately classify the great majority of ASD cases at such young ages. In sum, no currently available method matches the present invention for providing a combination of effectiveness across the youngest ages from birth to childhood; complex algorithmic use of gene weights, patterns and pathways in combination with clinical and neurobehavioral variables; high accuracy, specificity and sensitivity; and flexible utility in autism screening, diagnostic evaluation and prognostic assessment.

In certain embodiments, the invention provides methods of conducting a weighted gene and feature test of autism (WGFTA) for autism screening, diagnosis or prognosis. The method can include a) obtaining an analyte from a biological sample to obtain analyte-associated gene expression levels of a set of at least 20 or more genes selected from a model derived from an autism reference database, such as disclosed in Tables 1 and 2; b) statistically normalizing each expression level of the selected set of genes expressed to derive a normalized gene expression value (NGEV) for each gene in the selected set of the subject; c) preparing a weighted gene signature matrix (WGSM) of the selected gene set; d) calculating a weighted gene expression level of each gene in the selected set by multiplying the NGEV for each gene by a gene-specific weight of that gene. Gene-weights are derived from a computer-based bioinformatic analysis of the relative expression levels of at least the selected set of genes from the autism reference database including in certain embodiments at least 40 healthy individuals and 40 autistic individuals compiled in a weighted gene expression reference database (WGERD); and e) establishing the divergence of the set of each weighted gene expression level of the subject to the weighted gene expression reference database (WGERD), to thereby conduct WGFTA to indicate increasing correlation with autism risk, diagnosis or prognosis.

Genes that can be tested by the inventive method include those shown in Tables 1 and 2 and 16-25 herein. The genes can be selected based on their weighted relevance to diagosis or prognosis. These genes involve cell cycle, protein folding, cell adhesion, translation, DNA damage response, apoptosis, immune/inflammation functions, signal transduction ESR1-nuclear pathway, transcription-mRNA processing, cell cycle meiosis, cell cycle G2-M, cell cycle mitosis, cytoskeleton-spindle microtubule, and cytoskeleton-cytoplasmic microtubule functions. In certain embodiments, genes tested by the inventive method are involved in DNA-damage or mitogenic signaling in brain development.

In certain embodiments, the inventive method can use as few as 20 and include about 4000 Autism WSGM genes (including specific splice variants among these genes) which may be contained within as few as a single gene set or as many as 8 gene sets and subsets. Different sets and subsets can be used to optimize performance under different assay and application circumstances. In certain embodiments, genes are selected from at least 2, 10, 20, 25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 160, 320, 640, 762, or any number in between, for example, from the genes in Table 1. Table 1 represents genes in the present methods for selection based on the highest weight ranking which are more frequently associated with ASD diagnosis. The genes may be arranged and selected from among 4 sets as shown in Table 1, depending upon the commonality of their expression patterns. The top 50 genes with absolute value of weights ranging from about 0.50-1.00 in sets 1-4 are also listed in Tables 1.1, 1.2, 1.3, and 1.4.

In other embodiments, genes are selected from at least 2, 10, 20, 25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 100, 120, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500, or more genes in the gene listings as shown in Tables 16 through 25 provided below. In certain embodiments, the genes are unique differentially expressed (DE) genes found in ASD and control toddlers. These genes are for instance, dysregulated in DNA-damage response, mitogenic signaling, and cell number regulation.

In certain embodiments, normalized gene expression values of the signature genes (e.g., Tables 1 and 1.1-1.4) can be used as is, thus without weighting, for the classification of ASD vs non-ASD. In certain embodiments, using Boosting (see Scoring and Classification methods) three lists of genes were identified with the smallest number of elements that classified subjects with accuracy of at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, or 100%. Sets with 20, 25 and 30 features that can produce at least 70%, at least 75%, and at least 80% correct classification include but are not limited to those shown in Table 2 below. In certain embodiments, adjusting the weights of the genes based on the age of the subject is the most important single parameter for improving accuracy of ASD classification.

The invention claims the use of gene-weights and optionally, feature signatures as defined below for each of the WGSM genes that, when applied to an individual's actual gene expression can accurately predict that individual's risk for autism in screening or make accurate autism diagnostic or prognostic classification about that individual. It can also be used as a diagnostic test for autism for those already known to be at high risk for autism or suspected to have autism and other developmental disorders. It can also provide both a diagnostic classification prediction (autism, not autism) and an estimation of probability-risk for autism or other developmental disorders in newborns, infants, toddlers and young children.

The inventive method can further comprise an earlier step of obtaining an analyte in a biological sample, which refers to physically obtaining the analyte of interest in the biological sample directly from the body of a subject or physically moving a sample that has been previously taken from the subject. The biological sample can include but is not limited to, blood, cord blood, serum, plasma, cerebrospinal fluid, urine, tears, saliva, mucous, buccal swab, tooth pulp, skin, neuron, and any other bodily fluid, tissue or organ. The biological sample can also include cells obtained and/or derived from the biological samples and/or cell culture, including, but not limited to stem cells, fibroblasts, iPs, neuroprogenitor cells, and neural cells. In certain embodiments, the analyte includes, but is not limited to, DNA, RNA, protein, or metabolite in any biological sample. In certain embodiments, the analyte is blood-derived RNA from leukocytes. In certain embodiments, the WGSM applies weights gene-wise to an individual's normalized blood-derived (including a newborn's cord blood-derived) gene expression levels. Therefore, to screen and test for autism in newborns for example, the WGSM can be applied gene-wise to the individual's cord blood-derived RNA gene expression levels, and algorithms calculate autism risk. The WGSM is used alone or in combination with the other matrices discussed below. The elements contained in each of the other matrices can also be used as predictors in the diagnostic classification or prognostic analysis.

The inventive method therefore may also further comprise a comparison of a gene-network, including hub-gene network, signature matrix (GNSM) of the subject to the GNSM autism reference database, to establish a score for autism risk screening, diagnosis or prognosis based on the divergence of the subject's GNSM to the GNSM autism reference database. In certain embodiments, the GNSM comprises interaction patterns of specific gene-weights and features calculated from gene-to-gene interactions, including hub-gene interactions. The interaction patterns are calculated based on the relationship or state of a gene with non-genomic features.

The inventive method may also comprise a step of comparing a multi-modal signature matrix (MMSM) of the subject to the MMSM autism reference database, to establish a score for autism risk screening, diagnosis or prognosis based on the divergence of the subject's MMSM to the MMSM autism reference database. In certain embodiments, the MMSM is a matrix containing the quantification of non-genomic features obtained by clinical, behavioral, anatomical, and functional measurements. The non-genomic features comprise but are not limited to, age, a GeoPreference test score, a MRI/fMRI/DTI test, an Autism Diagnostic Observation Schedule (ADOS) test, or a CSBS test.

In certain embodiments, the invention is unique in utilizing a test based on specific age-weighted and age-change patterns and gene-weights of abnormal gene expression (for instance Weight Sets 1-4 in Table 1) in infants and toddlers with confirmed autism via longitudinal tracking. In certain embodiments, the invention provides a method specifically designed to leverage age-related gene expression differences between autistic and normal individuals in order to indicate probability risk for autism as it occurs at varying ages in the general pediatric population, making this a unique approach. Therefore, in certain embodiments the invention is a test based on the unique multidimensional gene and age weighted dataset of autism that is a reference standard for testing new patients/subjects at risk for autism across ages from newborns to young children. Thus, in certain embodiments, it can use age to transform values of elements in the WGSM and GNSM to improve the accuracy of tests for ASD based on the unique knowledge of how gene expression changes with age (e.g., in the first year of life) in ASD subjects. In certain embodiements, it can use age as a feature in classification (for example see Scoring/CLASS identity method below). Presented herein is the first evidence of age-related gene expression changes in any tissue that correlated with ASD at these early ages. In practice, each gene expression element in the WGSM and GNSM will change by a function of age, with functions ranging from age-independence to gain or loss of expression with decreasing age. These age dependent changes were determined and this information was used to adjust the weighting factors for each gene to age-appropriate weightings to enhance diagnostic performance at the age of individual patients.

Moreover, in some embodiments the invention provides a method further comprising a unique step of comparing a collateral feature signature matrix (CFSM) of the subject to the CFSM autism reference database, to establish a score for autism risk screening, diagnosis or prognosis based on the divergence of the subject's CFSM to the CFSM autism reference database. The CFSM comprises features collateral to the subject, for instance, the collateral features comprise analytes in maternal blood during pregnancy, sibling with autism, maternal genomic signature or preconditions, or adverse pre- or perinatal events.

In some embodiments, the invention further provides a method for autism preclinical screening, diagnosis or prognosis, comprising: a) obtaining a biological sample containing analytes of interest; b) preparing a weighted gene signature matrix (WGSM) comprising expression levels of a selected set of two or more analyte-associated genes selected from the genes listed in Tables 1-2 and 16-25; c) calculating a weighted gene expression level of each gene in the selected set by multiplying a normalized gene expression value (NGEV) of the WGSM by the gene-specific weight of that gene provided in Tables 1-2 and 16-25; and d) establishing the divergence of the set of each weighted gene expression level of the subject to a weighted gene expression reference database (WGERD), to thereby indicate increasing correlation with autism risk, diagnosis or prognosis. In certain embodiments, the WGSM is further processed to reduce dimensionality or computation time and increase power in the subsequent analysis steps.

In certain embodiments, using functional genomic and biological systems analyses, signatures of blood-derived RNA expression are derived from autism and subjects without autism that are patterns of “gene-specific-weights” (the WGSM) as well as patterns of gene-specific weights as a function of gene-gene interaction patterns (the GNSM), quantifiable features of the individual (e.g., age, sex, head circumference, neuroimaging measures, eye-tracking score; the MMSM) and collateral features (e.g., analytes in maternal blood during pregnancy, sibling with autism, adverse pre- or perinatal events; the CFSM). In essence, these genomic signatures transform the measured gene expression levels obtained from an individual through algorithm and knowledge-based selective application of the derived weighted-patterns that selectively enhance or diminish the impact of the measured levels on detection, diagnostic and prognostic classifications and risk estimates. The non-genomic feature matrices instead function as predictor variables.

In some embodiments, the invention therefore provides the use of these four derived signature matrices unified as the weighted gene and features matrix (WGFM) that is implemented as the weighted gene and feature tests for autism (WGFTA) for pediatric population screening for risk of autism and for autism diagnostics and prognostics in newborns, babies, infants, toddlers and young children. For example, its prognostics uses include prediction and characterization of likely clinical, neural and treatment progress and outcome. In certain embodiments, the WGFTA uses each in single or in any combination of the following four matrices of the WGFM: The Weighted Gene Signature Matrix (WGSM), The Gene-Networks Signature Matrix (GNSM), The Multi-Modal Signature Matrix (MMSM), and the Collateral Features Signature Matrix (CFSM). In particular embodiments, these signature matrices are designed to optimize, for example, screening for and detection of newborns and babies at risk for autism, while others are designed for use in the clinical evaluation and diagnostic confirmation of babies, infants, toddlers or young children previously identified as being at risk for autism, and in still others for use in the prognostic evaluation of probable clinical course (e.g., worse or improving clinical severity), later clinical outcome (later language, cognitive or social ability), or treatment response.

In some embodiments, the invention also provides a system for autism screening, diagnosis or prognosis, comprising a database generated model of at least two genes and corresponding gene-specific weights as provided in Tables 1-2 and 16-25, and instructions for use in applying the database to a weighted gene signature matrix (WGSM) comprising expression levels of a selected set of the same two or more genes expressed in a biological sample by a) calculating a weighted gene expression level of each gene in the selected set by multiplying a normalized gene expression value (NGEV) of the WGSM by the gene-specific weight of that gene provided in Tables 1-2 and 16-25; and b) establishing the divergence of the set of each weighted gene expression level of a subject to a weighted gene expression reference database (WGERD), to thereby indicate increasing correlation with autism risk, diagnosis or prognosis.

The invention is currently the only functional genomic test of autism that is based on direct experimental knowledge of the genetic functional effect and neural outcome defects that underlie brain maldevelopment in autism at varying young developmental ages, and the only autism genetic test that detects a majority of autism individuals. The invention is platform independent, and has been tested and validated on independent cohorts of patients and by using different methods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C. Gene Networks are associated with neuroanatomic measures variation and distinguish ASD from control toddlers. FIG. 1A) Total Brain Volume (TBV) measure distributions in ASD and control toddlers. T-test showed no statistically significant difference in the two distributions (pValue=0.645). FIG. 1B) WGCNA analysis across all ASD and control subjects together (combined analysis) identified seven modules of co-expressed genes that are associated with neauroanatomic measures (see also Table 5). The bar graph displays the enrichment scores of the seven modules using Metacore pathway analysis. FIG. 1C) The eigengene values of the same seven modules were used in a correlation analysis with the neuroanatomic measures (see Table 6). Overall six of the seven modules (gene networks) display statistically significant association with the neuroanatomic measures, but the association was different within each group. The scatter plots provide a graphical representation of the relationship between module eigengenes (gene expression variance) and total brain volume variation in the ASD (light grey) and control (dark grey) groups. The most evident differences between the two groups account for gene patterns in the cell cycle, protein folding and cell adhesion modules. Additional differences are found in the cytoskeleton, inflammation and translation gene modules. High expression levels of cell cycle and protein folding genes are found in normally small brains, while the other gene networks seem to have a weaker effect in keeping the brain from growing in size. Conversely, the combination of reduction in cell cycle and protein folding genes together with variations in gene expression levels in the other functional networks are found to drive pathological brain enlargement in ASD.

FIGS. 2A-2C. WGCNA analysis of the combined dataset (ASD and control together) defined which modules are associated with Total Brain Volume (TBV) measures in control toddlers and which modules in ASD toddlers. The impact of gene expression on brain size variation is calculated as Gene Significance (GS) for each TBV-associated module within control (dark grey) and ASD toddlers (light grey). The bar graphs show the difference in GS between the two groups. Negative GS values reflect the opposite relationship between eigengenes and TBV variation (see Table 6), thus high gene expression levels associated with small brain and vice versa. Solid bars with an asterisk indicate that the association is statistically significant. Empty bars without the asterisk on top indicate that the association is not significant. The correlation between the GS and Gene Connectivity (GC; defining hub-genes) for each gene within a module displays the change in activity patterns and impact on brain size variation of hub genes (left scatterplot for each module). The correlation between GS and Module Membership (MM; specificity of a gene to the assigned module) display consistent activity pattern changes relative to hub-genes alterations (right scatterplot for each module). The analysis of the top 30 genes for the three network features (GS, GC, MM) displayed that GS was the feature with the highest number of altered genes in each module. The module enriched in translation was overall the one with the highest number of genes that changed between ASD and control toddlers. FIG. 2A) Genes involving cell cycle and protein folding. FIG. 2B) Genes involving cell adhesion and cytoskeleton. 2C) Genes involving translation and inflammation.

FIG. 3. Co-expression modules generated from the WGCNA analysis of control and ASD samples separately. The absolute values of GS for control-based modules (left) are consistent with the modules from the combined analysis within the control group (FIGS. 2A-2C). The absolute values of GS for ASD-based modules (right) are consistent with the combined analysis within the ASD group (FIGS. 2A-2C) and displayed an increase in the number of modules associated with TBV measures. The differences in modules associated with TBV measure in the separate WGCNA analysis are hence accentuated.

FIGS. 4A-4E. Pathway-based Replication analysis of differentially expressed (DE) genes. Module-based classifier efficiently distinguishes ASD from control subjects and displays a high protein-protein interactions (PPI) enriched in translation genes. FIG. 4A) Pathway enrichment comparison in Metacore between the Discovery and Replication DE genes. DNA-damage and Mitogenic signaling share the strongest similarity. FIG. 4B) Pathway enrichment analysis of the commonly dysregulated genes in both Discovery and Replication samples. FIG. 4C) Left panel, ROC curves and AUC values from the classification of Discovery (ROC 1) and Replication (ROC 2) subjects. Right panel, ROC curves and AUC values from the classification of all subjects in the different diagnostic categories. ROC 3=ASD vs typically developing (TD) toddlers (thus excluding contrast subjects); ROC 4=ASD vs contrast toddlers; ROC 5=contrast vs TD toddlers. FIG. 4D) Coordinates extracted from all ROC curves in panel C. FIG. 4E) Cytoscape visualization with the PanGIA module style using the genes from the four modules with direct PPI (DAPPLE database). The number of interactions is correlated with the color and position within the network. White indicates <8 PPI; yellow to red indicates 8≤PPI<31. The core of the network, represented by the genes with the highest number of interactions, is enriched with translation genes.

FIG. 5 WGCNA analysis across ASD and control toddlers. Co-expression modules are generated and color-coded (here showed in grey scale). Each vertical line corresponds to a gene, and genes with similar expression are clustered into modules. Modules are herein called by the assigned WGCNA default colors. Module eigengenes are computed for each subject and each module.

FIGS. 6A-6B Correlation analysis between modules and neuroanatomic measures using WGCNA on all discovery subjects. pValues are in parentheses. Dx=diagnosis, L=Left, R=Right, CB=Cerebrum, CBLL=Cerebellum, GM=Gray Matter, WM=White Matter, TBV=Total Brain Volume, hemi=hemisphere, SA=Surface Area, BS=Brain Stem. FIG. 6A) MEDARKRED-MESALMON. FIG. 6B) METAN-MEGREY.

FIG. 7 Gene Significance (GS) to Gene Connectivity (GC) correlation within each module in the ASD and control groups. 12 of the 22 co-expressed modules across groups displayed a severe change in pattern direction (negative to positive or not significant correlation), while 4 modules had a modest change in correlation (same direction).

FIGS. 8A-8B Association analysis between modules and neuroanatomic measures using WGCNA on control toddlers. L=Left, R=Right, CB=Cerebrum, CBLL=Cerebellum, GM=Gray Matter, WM=White Matter, TBV=Total Brain Volume, hemi=hemisphere, SA=Surface Area, BS=Brain Stem. FIG. 8A) MEMAGENTA-MEGREEN YELLOW. FIG. 8B) MEGREY60-MEGREY.

FIGS. 9A-9B Association analysis between modules and neuroanatomic measure using WGCNA on ASD toddlers. L=Left, R=Right, CB=Cerebrum, CBLL=Cerebellum, GM=Gay Matter, WM=White Matter, TBV=Total Brain Volume, hemi=hemisphere, SA=Surface Area, BS=Brain Stem. FIG. 9A) MELIGHT GREEN-MEDARKRED. FIG. 9B) MEGREEN YELLOW-MEGREY.

FIG. 10 WGCNA analysis across ASD and control toddlers using the differentially expressed genes. Co-expression modules are generated and color-coded (here showed in grey scale). Each vertical line corresponds to a gene, and genes with similar patterns are clustered into modules. Modules are herein called by the assigned WGCNA default colors. Module eigengenes are computed for each subject and each module.

FIG. 11 Plot of classifier prediction performance relative to subject's age. Distribution of subject age separated by the accuracy of the classifier.

FIG. 12 Plots of the prediction performance and age-corrected total brain volume (TBV), whole cerebrum and cerebellum measures.

FIG. 13A-13C. Age- and diagnosis-related gene expression profiles. FIG. 13A) example of change in gene expression with a main effect of diagnosis (ASD in light grey vs Control in dark grey). FIG. 13B) example of change in gene expression with main effects of age and diagnosis. FIG. 13C) example of change in gene expression with interaction between age and diagnosis.

FIGS. 14A-14B. Inclusion of age in the classification analysis using Boosting. FIG. 14A) Graphical representation of the classification outcome in the training set (continuous line) and after cross-validation (dotted line) with age as additional predictor. FIG. 14B) Graphical representation of the classification outcome without age as predictor. When using age as additional predictor the cross-validation error diminish from about 0.3 (30%) to about 0.2 (20%), thus suggesting that age is helpful in improving classification accuracy.

FIG. 15. Diagram representing the splits of decision tree classification (left panel) for ASD (+1) and control (−1) and the feature space that is recursively divided into finer sub-regions accordingly to the number of feature used (right panel).

FIG. 16. Diagram representing the boosting algorithm (for example AdaBoost) by fitting a baseline classifier and using its performance on the training data to re-weight the importance of each point in subsequent fits.

FIG. 17. Boosting classification performance using 25 genes of the signature matrix. The cross-validation error is about 25%, thus giving a classification accuracy of 75%.

DETAILED DESCRIPTION OF THE INVENTION

Various publications, including patents, published applications, technical articles and scholarly articles are cited throughout the specification. Each of these cited publications is incorporated by reference herein, in its entirety.

Throughout this specification, the word “comprise” or variations such as “comprises” or “comprising” will be understood to imply the inclusion of a stated integer (or components) or group of integers (or components), but not the exclusion of any other integer (or components) or group of integers (or components).

The singular forms “a,” “an,” and “the” include the plurals unless the context clearly dictates otherwise.

The term “including” is used to mean “including but not limited to.” “Including” and “including but not limited to” are used interchangeably.

In some embodiments, the invention provides a use of functional genomic signatures in combination with functional genomic-based multimodality signatures in screening for autism risk and in autism diagnostics and prognostics. The multimodality signatures include, but are not limited to, physical, neurobehavioral, neuroimaging, neurophysiological, clinical history, genetic, maternal precondition, parent questionnaire, family history and behavioral and psychometric test information, and derived from bioinformatic and biological systems analyses of analytes collected in vivo from peripheral tissues including cord blood, blood, skin and urine. The invention specifically provides the use of varying forms of such signatures each tailored to optimize autism screening, diagnostics and prognostics according to the individual's age, sex, ethnicity, and clinical and family history, thus, providing pediatric population screening biomarkers of risk for autism and diagnostic and prognostic biomarkers of autism (i.e., Autism Spectrum Disorders or ASD as defined in DSM V and broadly characterized in DSM-IV-TR) and risk for autism in individuals at young ages, including newborns, babies, infants, toddlers and young children. Prognostic biomarkers as used herein include those that predict and characterize likely clinical, neural and treatment progress and outcome.

In certain embodiments, the invention can test for risk of autism in any newborn, infant, toddler or young child. The functional genomic and functional genomic-based multimodal signatures presented here, developed from general pediatric populations at young ages, have far better accuracy, specificity and sensitivity than any previously developed biological- or behavior-based screen or early classifier in ASD newborns and 0 to 1 year olds 1 to 2 year olds, 2 to 3 year olds and 3 to 4 year olds. In particular embodiments, the invention provides computer-based bioinformatics analyses that have derived genomic and genomic-based multimodal signatures in vivo that efficiently predict autism at very young ages.

Because autism is a strongly genetic disorder of neural development, a major breakthrough in risk assessment of autism would be the ability to identify functional genomic defects that relate to and may underlie brain development in autism at the youngest ages possible. From such gene-brain knowledge, better and more autism-relevant biomarkers of early risk should be obtainable. Therefore, in some embodiments the invention provides unique analyses not performed previously by any other researchers in the autism field that identified functional genomic defects in blood leukocyte mRNA that are strongly correlated with brain and cerebral cortex developmental size in very young autistic subjects. In certain embodiments, the invention provides that among the genes so involved, a large percentage of them are also abnormally dysregulated as compared to the typically developing control infants and toddlers. This result is the first identification of a functional genomic pathology in the first years of life in autism. Using bioinformatics and systems biology analyses, the invention provides functional genomic and functional genomic-based multimodality signatures (the weighted gene and features matrix) for autism screening, diagnosis and prognosis, which is used in the invention of the weighted gene and feature tests of autism (WGFTA).

The WGFTA of the invention detects, quantifies risk and classifies autism, and other developmental disorders at the youngest ages in the general pediatric population with greater accuracy, specificity, sensitivity and positive predictive value than any other published method. These are the first clinically-relevant, brain development-relevant and practical genomic signatures of risk for autism in newborns, infants, toddlers and young children. This set of signatures detects subtypes of autism with more severe as well as less severe involvement. As such, the WGFTA impacts identification of those with more severe neuropathology and reveals differential prognosis. Moreover, repeat testing with the WGFTA enables tracking and understanding longitudinal changes in autism neural and clinical pathology across development in autism. Not only does the invention of WGFTA set of tests have substantial clinical impact at the level of the individual child—a first in the autism field, but the invention also impacts studies linking genetic and non-genetic etiological variables in this disorder.

More detailed descriptions of the invention of WGFTA and associated signature matrices are provided below. In certain embodiments, the invention provides the weighted gene feature tests of autism (WGFTA), which is the application of each single or any combination of the following matrices, unified under the name “Weighted Gene and Features Matrix” (“WGFM”):

The weighted gene signature matrix (WGSM) is a matrix containing sets of genes and gene-weights, which constitutes a model of referenced dataset. In certain embodiments, gene weights are derived from a computational bioinformatics analysis of the relative expression levels of at least the selected set of genes from more than 40 healthy individuals and 40 autistic individuals compiled in a weighted gene expression reference database (WGERD). In certain embodiments, the invention provides a WGSM and/or WGERD with at least 2, 10, 20, 25, 30, 35, 40, 50, 60, 70, 80, 160, 320, 640, 762, 800, 900, 1,000, 1,500, 2,000, 2,500 or more genes, or any number of genes and their respective weights determined as described and exemplified herein. The genes can be arranged into sets of common expression patterns, as an example, 4 sets are shown in Table 1. In certain embodiments, the referenced database WGERD of the invention is designed to be constantly updated with new subjects and additional features (e.g., sequencing data) so that the genes and gene weights, as well as non-genomic features can be updated accordingly.

The weights of genes provided in Table 1 can be rounded to the nearest 1/10; 1/100; 1/1,000; 1/10,000; 1/100,000; 1/1,000,000; 1/10,000,000; or 1/100,000,000. The genes are provided in ranked order of their weighted correlation as provided in Tables 1.1 through 1.4.

One computer-based bioinformatic algorithm used to determine the weighted values is part of the Weighted Gene Co-expression Network Analysis (WGCNA) package in R computer environment (cran.us.r-project.org/). The use of this package is also described in example 1 and 2 methods (see below). Quantification of gene expression levels and therefore weight calculations are platform and method independent. Microarray-based platforms (for instance, Affymetrix, Illumina Nimblegen chips), sequencing-based reactions (for instance Illumina or Roche next-generation seq or traditional Sanger seq) and any other quantitative approaches (for instance qPCR-based such as the Fluidigm system) can be used to determine nominal gene expression levels and with any of the weight calculation methods described herein. Using recommended settings in the WGCNA package, cleaned and normalized gene expression data is clustered into gene sets (herein called Modules) based on similarity of co-expression. Genes with similar expression patterns across subjects are assigned to a specific module. For each module and each subject an eigengene is then calculated. Calculation of the eigengene values is done via the computer formula “moduleEigengenes (data)” where “data” is the variable containing the gene expression values of all subjects. This step is equivalent to the conventional principal component analysis in which the variance of a multi-dimensional dataset (many genes) is represented by one value (component 1 or eigengene value). The weights are then calculated by using the “cor( )” formula in R with “data” and “eigengenes” as arguments. This function performs correlation analysis between a module eigengene value and the expression value of each gene within the same module. Correlations are performed for all genes in a module and for all modules. Using this method, weights values range from −1 to 1, and represent the contribution of genes to the overall gene expression variance of each particular module. Genes with weights values closer to −1 and 1 have the highest contribution, thus importance. Weights are calculated also using other analogous data-reduction methods that may or may not include a priori clustering steps as to the case of WGCNA (based on co-expression). Examples are Principal Component Analysis (PCA), Multi-Dimensional Scaling (MDS), and Independent Component Analysis (ICA). In these examples, weights are commonly referred as “Loadings”. Weights calculation is extended also to the use biological information, such as protein-protein interactions (PPIs), gene-to-gene interactions (GGIs), Gene Ontology (GO) information, and network or ranking position; therefore both statistical and biological-based methods can be applied to derive weights/loadings from gene expression data.

The present invention provides, for the first time, the use of weights in the screening and diagnosis of autistic subjects, especially at young ages (birth to age 4 years). Autism involves disrupted hub genes and gene pathways, sub-networks, networks and modules (see EXAMPLE 1), and the patterns of less to more abnormal gene expression within these systems is encoded and used for autism screening and diagnostics in the invention. In some embodiments, this is done via PPIs and/or GGIs as just stated and such pattern information is in the GNSM. In other embodiments, the patterns of less to more abnormal gene expression are encoded by gene weights. This gene-weighting improves performance, and can be used in combination with classifying genes into modules or independently of modules. Similarly, clustering the genes into modules can be used alone or in combination with GNSM, MMSM, and CFSM.

The study of the unique reference dataset of ASD and control infants and toddlers provided the unique opportunity to discover importance levels of genes (from low to high priority) for the identification of autism risk. Based on the biological information present in our reference dataset, genes with higher priority have a higher importance in correctly classifying ASD patients. Priority was assigned based on the weight value calculated for each gene. As described above, genes with weights values closer to −1 and 1 have the highest contribution (and, thus importance). Therefore, gene lists can be selected based on the weight values. For example, in some embodiments, a gene list can be selected from genes with an absolute weight value of 0.15 to 0.4, from 0.5 to 0.7, or above 0.8. In certain embodiments, a gene list can be generated by selecting genes with an absolute weight value of above 0.15, above 0.20, above 0.25, above 0.30, above 0.35, above 0.40, above 0.45, above 0.50, above 0.55, above 0.60, above 0.65, above 0.70, above 0.75, above 0.80, above 0.85, above 0.90, above 0.91, above 0.92, above 0.93, above 0.94, above 0.95, above 0.96, above 0.97, above 0.98, or above 0.99. The genes can be selected with or without using clustering to define particular modules before applying the weighting. The four “Top 50” gene sets show weights ranging from approximately 0.53 to 0.98 for the top 50 genes of four different modules. Alternatively, the absolute weights can be used as a threshold (with or without clustering) to determine a number of genes having a weight above, for example, any of the absolute weights listed above.

TABLE 1 GeneID_set 1 Weights Set 1 GeneID_set 1 Weights Set 1 GeneID_set 2 Weights Set 2 GeneID_set 3 Weights Set 3 GeneID_set 4 Autism Dx Set 4 CD3D 0.935783593 C2orf3 0.643325498 CUTL1 0.899346773 LOC44926 0.952217397 SDPR 0.982476 UXT 0.899396969 PDCL 0.641692741 MAST3 0.891222232 ITM2B 0.918889397 PDE5A 0.936387 RPS4X 0.897584727 ZNF544 0.641399133 STK4 0.858164628 HOXC6 0.899386247 PTGS1 0.885464 LOC283412 0.893348386 PAK1IP1 0.641392622 KIAA247 0.842925274 LOC392288 0.895514994 CTDSPL 0.883295 LOC127295 0.891939246 LOC4455 0.639352929 MYH9 0.829782866 YIPF4 0.891398192 CTTN 0.88314 LOC42694 0.891528532 PAQR8 0.639332216 RAPGEF2 0.817284111 RBMS1 0.888876658 ALOX12 0.872153 SKAP1 0.891256553 TMEM5B 0.638786733 ARAP3 0.815255748 USP6 0.871882682 MPL 0.867286 LOC72882 0.889935118 C22orf32 0.636925896 RAB11FIP1 0.797221635 KIAA133 0.868831295 DNM3 0.856197 LOC645173 0.888169553 CXCR7 0.63651849 WBP2 0.796352118 LOC642567 0.867721395 C1orf47 0.848722 RPL23A 0.887966784 RTBDN 0.635348948 GNAI2 0.795553329 EVI2B 0.865616513 C7orf41 0.828971 LOC646942 0.883945464 EEF1G 0.634822364 MTMR3 0.795286983 UBE2W 0.851386357 C5orf4 0.827413 LOC646294 0.881255183 RPL37 0.633756743 CBL 0.792889746 DDX3X 0.849399278 RAB27B 0.815966 LOC728428 0.881254479 KIAA355 0.63224944 UBE4B 0.792663385 UBE2D1 0.844426485 CXorf2 0.811991 LOC44737 0.871745735 MRPS27 0.631945943 IGF2R 0.791413796 HIAT1 0.841479672 GRAP2 0.797727 LOC7329 0.871551112 SSR4 0.629113287 YPEL3 0.789244232 TTRAP 0.837658199 CDC14B 0.782988 LOC391833 0.868862845 TOMM7 0.628843995 SETD1B 0.784239228 LOC44525 0.83588163 DAB2 0.771423 RPS3 0.867774287 LOC1131672 0.628653337 PIK3CD 0.782776396 C18orf32 0.828614134 TAL1 0.755586 RPL36 0.864561634 KRT73 0.628159669 RASSF2 0.775818951 LOC1132888 0.826453522 NCALD 0.747679 LOC1127993 0.86394855 POLR1D 0.625815554 KDM6B 0.774456479 ROCK1 0.821534228 ITGB5 0.74494 LOC73187 0.862255251 INPP4B 0.625699386 TP53INP2 0.769228192 LOC64798 0.818875634 GUCY1A3 0.732784 LOC72831 0.861398415 ALKBH7 0.623546371 NUAK2 0.764937137 FAM91A2 0.8151116 FERMT3 0.725864 LOC653162 0.857976375 AKR7A3 0.622561242 PAK2 0.764551187 SENP6 0.81341925 TSC22D1 0.725234 LOC729679 0.856634917 OGFOD1 0.622236454 MYO9B 0.758257515 LOC732229 0.812945123 LIMS1 0.722976 LOC441246 0.856563215 COX7A2L 0.622161758 NDE1 0.757911755 CEP63 0.812863172 SLC8A3 0.721372 LOC387841 0.853626697 SNORD16 0.619846554 IRS2 0.748758318 ATG3 0.811914569 ABCC3 0.716486 C13orf15 0.853475792 PRKCA 0.619788175 PHF2 0.747211227 LOC1128269 0.79772624 HOMER2 0.713716 LOC728576 0.852817639 MAN1C1 0.617174926 MAP2K4 0.746288868 PLAGL1 0.79624413 NAT8B 0.712372 EIF3K 0.851382429 COX11 0.617173924 CAMK1D 0.743845616 MBD2 0.794667574 FBLN1 0.695683 EEF1B2 0.848139827 EDAR 0.616832496 CDC2L6 0.739975446 EXOC8 0.789627347 ARHGAP21 0.688976 LCK 0.847839497 SMYD2 0.615536584 ASAP1 0.734296313 MRRF 0.788483797 C21orf7 0.688378 LOC39345 0.846855595 C2orf196 0.615182754 TSC22D3 0.729781326 LOC113377 0.785194167 C15orf52 0.687782 RPL4 0.846548149 ACYP2 0.61357462 TLN1 0.728642978 POTE2 0.784824825 CABP5 0.682826 LOC1132742 0.842833281 GCET2 0.613272559 ANXA11 0.727162993 C8orf33 0.783663596 ENDOD1 0.663152 EIF3H 0.842637899 SNORD13 0.612937581 EP3 0.726274852 LOC38953 0.78121382 SOCS4 0.66279 CD27 0.842195295 C1orf14 0.612368536 ROD1 0.725977368 CPEB3 0.773273834 C15orf26 0.644173 RPS15 0.841763438 LOC647276 0.611266653 RXRA 0.725773276 C6orf211 0.769958713 PVALB 0.638495 LOC649447 0.839379287 PLEKHF1 0.598224876 RASSF5 0.721366824 LOC1128533 0.769836835 SLC24A3 0.637579 LOC1131713 0.838956452 FKBP14 0.598185865 PELI2 0.719737185 LOC648863 0.769661334 HGD 0.635255 LOC286444 0.837678151 FOXO1 0.597245265 SEMA4D 0.717738781 STX7 0.767879114 ZNF185 0.628879 LOC729789 0.837517553 LOC339352 0.594815425 PPM1A 0.716812485 14-Sep 0.764666259 CA2 0.624763 RPL1A 0.828892451 ZNF395 0.594235542 CREBBP 0.716647465 LOC4493 0.763746743 CXCL5 0.618479 CD6 0.826562257 DSTN 0.592351455 LAPTM5 0.716353697 LOC442319 0.763346856 GRB14 0.617611 LOC646766 0.824653268 RPS29 0.591862228 CABIN1 0.715925162 NCRNA81 0.751898522 VWF 0.611157 C17orf45 0.823936864 SNORD21 0.591444476 PLCB2 0.715345575 CLEC7A 0.744233541 DKFZp686I15217 0.599262 CUTA 0.823637548 LOC64663 0.589558195 WNK1 0.711353632 CSNK1A1L 0.733761775 NDUFS1 0.593178 EIF3F 0.82286832 TBCA 0.588271553 BCORL1 0.698292888 LOC643896 0.731569432 GRASP 0.581414 LOC642741 0.822312667 PLAG1 0.586638621 SIK3 0.697558261 P74P 0.725849778 RGS18 0.572236 LOC388339 0.821936639 TTC39C 0.585818195 SLC44A2 0.696529915 GABARAPL2 0.723517197 C16orf68 0.562993 RPS14 0.821669767 ZNF16 0.585192385 EPOR 0.692878472 FCGR3A 0.717268353 MGC135 0.552543 LOC11398 0.818423753 LOC645233 0.584896119 SP2 0.686587522 LOC65638 0.714919235 LOC64926 0.548172 LOC643531 0.818367581 CENPL 0.58453599 IP6K1 0.686339387 FAM126B 0.713524823 HIST1H2AE 0.53314 LOC642357 0.815418616 XYLT2 0.583954832 LPIN2 0.686253547 TOP1P2 0.711774489 TCEA3 0.472277 LOC4455 0.815254917 TSPAN5 0.581829618 TGFBR2 0.681731345 TFEC 0.697721596 MEIS1 0.453958 RPS5 0.81396843 LOC4464 0.578616713 MYST3 0.67599148 HERPUD2 0.692843953 MSRB3 0.448888 PIK3IP1 0.812422946 HABP4 0.578161674 MID1IP1 0.675927736 RPAP3 0.689713938 DNHD2 0.448113 RPL5 0.799548493 NHP2 0.577712263 AHCTF1 0.675368429 LOC644964 0.688291553 IRX3 0.396578 FLT3LG 0.798617496 SELM 0.571396694 CHES1 0.675156518 LOC391769 0.673227357 SPG21 0.389869 ATXN7L3B 0.798521571 DCXR 0.56883363 MAP1LC3A 0.673939379 BRD7P2 0.664481299 SPC25 0.374118 DKFZp761P423 0.797275569 PHB 0.56679772 KDM5B 0.673634194 ANP32A 0.662291765 POLR1E 0.795479112 CD32 0.565674142 ZYG11B 0.673297864 LOC641992 0.647881441 C2orf89 0.794392985 DLEU1 0.564859273 POLR2A 0.665496976 PAPSS2 0.637828688 C11orf2 0.793512166 DUSP14 0.562495337 AKT1 0.663541972 LOC1128627 0.637538136 FAM1A4 0.793287257 MSX2P1 0.559554447 TBL1X 0.662364885 KRT8P9 0.63712914 LDHB 0.791745887 RNF144A 0.559297465 IMPA2 0.65781512 TMX4 0.612694353 LOC73196 0.791625893 AHCY 0.558954772 ATG2A 0.654245217 LOC64552 0.59882446 LOC44927 0.789154863 FAM134B 0.558375382 MAPKAPK2 0.653979578 LOC389286 0.596113393 TNFRSF25 0.786317228 TYSND1 0.556848766 FAM11B 0.649818256 CWC22 0.592755277 ZNF329 0.782992446 LOC728953 0.554168254 CENTB2 0.648915988 SH3BP2 0.55771393 LOC644464 0.779129219 LOC387791 0.551536874 RFX1 0.648867183 LAPTM4A 0.551788533 RAB33A 0.776193173 SELPLG 0.549789855 SPI1 0.642942512 SYTL2 0.499767546 RPL22 0.775782518 KLRB1 0.548467766 ZNF281 0.641915681 ANP32C 0.378151333 LOC388564 0.774155475 ATP5E 0.547577933 USP9X 0.641791596 LOC1134291 0.277985424 C6orf48 0.772942779 TCP1 0.547495293 DPEP2 0.641158453 LARP1 −0.312422458 DDHD2 0.772697886 ZDHHC9 0.544612934 PACS1 0.636214668 C18orf1 −0.315553843 PKIA 0.771777911 CCDC72 0.543531769 GATAD2B 0.631961987 TCEAL4 −0.394177985 C11orf1 0.77146654 RNF144 0.543479417 MGC42367 0.631548612 SDHAF1 −0.415292518 RWDD1 0.769315667 MARCKSL1 0.543422113 PJA2 0.629172534 CCDC9A −0.418249658 LOC389342 0.769266259 GPX4 0.541737879 BRD3 0.628793665 ODC1 −0.488451364 CA5B 0.768742497 VSIG1 0.539617567 KIDINS22 0.622713163 ARHGAP1 −0.495153647 DAP3 0.765349952 DHRS3 0.538953789 FAM12A 0.59691644 TADA1L −0.517862143 ATPGD1 0.765166323 CNNM3 0.537386642 RAB11FIP4 0.596547435 LOC92249 −0.579379826 C12orf65 0.764854517 FBLN2 0.535467587 OSBPL8 0.593855675 CD99 −0.59333825 ATP5A1 0.7645682 ELOVL4 0.535114973 CCNK 0.592217195 HCST −0.625513721 IL27RA 0.763477657 PRRT3 0.534237637 SGK 0.588593659 TRAPPC4 −0.643976448 ORC5L 0.762996289 VHL 0.532395335 PCBP2 0.586773694 EIF2AK1 −0.644486837 MFNG 0.761418624 HNRNPU 0.531745499 SNORA28 0.584584438 CS −0.653859524 APOA1BP 0.759114222 FCGBP 0.527263632 C14orf43 0.573927549 LOC1128731 −0.654961437 USP47 0.758717998 GOLPH3L 0.527213868 ELMO1 0.571788753 ILVBL −0.655857192 PEX11B 0.754628868 LMNB2 0.524692549 TMCC1 0.566173385 SETD1A −0.662596368 CRBN 0.754152497 CCT3 0.524567526 DGCR8 0.564982984 LOC4948 −0.724984343 C12orf29 0.753564787 CRIP1 0.52227375 NCOR2 0.563615666 TTC4 0.752585135 ZFP3 0.517155756 UBAP2L 0.558982967 C1QBP 0.752379867 PEBP1 0.515338931 PRKCB 0.556183699 LOC728128 0.751472664 9-Sep 0.514442369 SEC16A 0.555783769 GDF11 0.74939769 TSTD1 0.51172194 C13orf18 0.555593833 C16orf53 0.748642633 SNHG9 0.498816845 HNRPUL1 0.54417842 LOC347292 0.748154744 NDUFAF3 0.493661179 LASP1 0.543199946 EIF3L 0.747991338 ACOT4 0.493494423 SF3A1 0.537427512 QARS 0.746682333 LIAS 0.493133496 HELZ 0.532982164 TCEAL8 0.738139918 ST6GALNAC4 0.492572367 ABAT 0.532615683 LOC4963 0.737889313 C1orf35 0.491497922 PRKCB1 0.531452289 LOC25845 0.73723136 KIAA143 0.489514968 NCF1B 0.528432749 SMYD3 0.734452589 TIMM22 0.489238613 CUGBP2 0.526196965 MGC87895 0.733843872 TMEM116 0.489235392 ANGPT1 0.523883946 SEC62 0.733293263 DBP 0.488445622 MAPRE3 0.522517685 PRAGMIN 0.731919211 TMEM17 0.487266629 DAPK2 0.521285458 LOC73246 0.731324172 C22orf29 0.485679679 NLRX1 0.518491497 ABHD14A 0.729919691 WDR82 0.47897466 GATAD2A 0.515499364 LOC729279 0.729691569 C2orf15 0.477568944 NR4A2 0.514797225 RAPGEF6 0.729549364 AK5 0.476192334 JARID2 0.514354883 C19orf53 0.728514239 AKTIP 0.474998212 GATS 0.499114393 LOC44113 0.728397238 ZBED3 0.474981147 ARID4A 0.492115532 HSPB1 0.726571375 SH3PXD2A 0.46973856 CHPF2 0.489985486 GPN1 0.726566569 NENF 0.468411812 EPN2 0.488233339 SLC25A3 0.726435744 TGIF1 0.467594838 TMEM33 0.482944342 POLR2G 0.726261788 ZNF559 0.465617668 AGAP8 0.477844348 SUMF2 0.726147864 MMGT1 0.461621563 ATP2B4 0.477783292 GLTSCR2 0.725737593 ZNF252 0.458778973 DIAPH1 0.471135458 LOC6473 0.724122215 PRUNE 0.457978567 METTL9 0.469936938 FBXO32 0.722432538 LOC646836 0.457692454 HSPA1L 0.469749354 TSGA14 0.719854653 LDOC1L 0.457631285 LOC113383 0.468335258 MDH2 0.718886435 CRIP2 0.455459825 KBTBD11 0.46717114 RPS8 0.716652755 ARRDC2 0.453694329 BRPF3 0.465177555 SEPW1 0.716486338 AP2S1 0.452824193 UBE3B 0.461616795 FAM3A 0.715548165 LRRC16A 0.442652223 CD3LB 0.4557268 MAL 0.71483775 CDC42SE2 0.439226622 PAN3 0.455644279 EIF3G 0.713911847 LARGE 0.433862128 TACC1 0.451198563 LOC653737 0.713386474 LOC642755 0.429488267 RAB43 0.449652328 LOC1129424 0.713323277 LOC729985 0.427293919 CLASP1 0.447777232 PLCG1 0.712268761 SERPINE2 0.426932499 FLJ1916 0.445564612 TMEM23 0.711859266 LOC1128252 0.425642295 PDPK1 0.444597997 LYRM7 0.711826946 LOC64634 0.422889942 FAM65B 0.44427143 COMMD7 0.711479625 RTKN2 0.421221582 ARID1A 0.442712377 TECR 0.711389973 ZFP14 0.413753175 DACH1 0.439734865 C16orf3 0.696214995 DECR2 0.392967984 SREBF1 0.429394886 PECI 0.694839698 ZNF24 0.39244688 SRRM2 0.423932667 LOC646688 0.694445665 HPCAL4 0.392296965 ZFYVE27 0.421452588 C1orf151 0.691589944 NT5DC3 0.385937142 TAF4 0.418321979 LOC72942 0.689981631 SNORD18C 0.377829771 RNF13 0.417795315 BTBD2 0.689831116 C19orf39 0.377367974 ZNF644 0.4159187 LOC645515 0.6893317 CNN3 0.374713277 CCDC97 0.399889593 SMPD1 0.688971964 PDZD4 0.371554119 MED31 0.392396434 PPP1R2 0.688489262 LOC652837 0.364795947 NCRNA85 0.382898142 NMT2 0.688136554 KIAA226 0.361262176 ANKRD12 0.382668594 PPM1K 0.687738718 C2orf1 0.354822868 LOC64235 0.382215946 LOC731365 0.686367864 C3orf1 0.354718283 FNBP1 0.36114745 RSL1D1 0.685958983 LOC64331 0.354694241 TWSG1 0.351262263 EEF2 0.685894553 PLD6 0.348358154 AHNAK 0.341474449 PIN1 0.685299297 GSTM3 0.347442317 CMTM4 0.33968982 MTCP1 0.684631822 CBR3 0.322565348 EPAS1 0.335925656 LYRM4 0.683961594 CAMSAP1L1 0.321123437 FAM19A2 0.331599374 LOC439949 0.682459967 C21orf33 0.316181939 BMPR2 0.265431535 MOAP1 0.679537354 ZNF773 0.294777162 C5orf53 0.251347985 NIP7 0.678675569 POTEE 0.294494551 OR7E156P −0.215227946 IFFO2 0.677846416 ELA1 0.293626752 LOC1132493 −0.281312391 NUCB2 0.677791323 SPNS3 0.28537988 SIL1 −0.286555239 MAGEE1 0.677713541 AKR1C3 0.27769758 BCL2L11 −0.341419371 LOC1131662 0.677193155 CCDC23 0.263623678 UHRF2 −0.354936336 MRPS15 0.675764332 GSTM2 0.257679191 PARP15 −0.37762429 NOG 0.675741187 DNTT 0.242897277 SGOL2 −0.411241473 POLR3GL 0.675617726 ACSM3 0.241276627 LOC644482 −0.415712543 RPL17 0.675285949 ZNF683 0.231965799 NCKAP1L −0.418321587 AK3 0.674199622 LAPTM4B 0.228282129 HCFC1R1 −0.449654339 IL23A 0.672979677 C6orf16 0.225251342 LOC92755 −0.452596549 ALDH5A1 0.671134823 GSTM4 0.215359789 BATF −0.463729569 ZNF54 0.667378217 PFKFB3 0.213262843 LOC729779 −0.468574718 SFRS2B 0.667128489 PEMT 0.188677328 ING3 −0.479326333 LOC649821 0.663376153 TOX2 0.157468472 LOC64746 −0.51635382 LPAR5 0.661938675 LOC72949 −0.198886269 LOC644745 −0.516637429 ZNF792 0.661844441 TROVE2 −0.229347861 SERPINB8 −0.523912813 CD4LG 0.659346237 MPDU1 −0.236721729 C15orf57 −0.524154265 LOC147727 0.658543639 BRWD2 −0.272935165 SLC25A19 −0.533627461 FAM12A 0.658423623 ANKRD41 −0.278587817 GNG7 −0.541637763 SLC25A23 0.65773867 WASH2P −0.283377589 CEPT1 −0.568436894 GLRX5 0.655646442 ECT2 −0.326623195 RPS7 −0.573623857 HIGD2A 0.654182518 LGSN −0.351114879 MRPL41 −0.578622978 ZNF26 0.653666419 CLEC12A −0.35367923 CCDC28B −0.58366246 NFX1 0.653548398 LOC44264 −0.384431314 PSMB7 −0.586314985 NELL2 0.653478218 AP1G1 −0.389494962 LOC644877 −0.587312525 NDUFB11 0.653473711 ADCY7 −0.427648158 TCEB1 −0.614147656 CCDC65 0.651898138 MIR1974 −0.429379598 CKS2 −0.619366364 ZNF518B 0.651475739 CTRL −0.448386681 THOC4 −0.625798657 TCEA2 0.649342463 LOC42112 −0.453281873 LOC113181 −0.636264657 LOC113291 0.649229319 ANXA2P3 −0.457511395 LOC7292 −0.648413997 PABPC4 0.649134234 LOC1133875 −0.459712358 MRPL17 −0.672451965 EIF2S3 0.648894172 HM13 −0.461368312 DBI −0.689455395 RPS18 0.646475474 CD74 −0.465298864 LOC113932 −0.717395773 STAT4 0.646221522 LILRA3 −0.467695852 ETFB −0.734397533 CCDC25 0.644689569 ARHGAP3 −0.469736658 NUDCD2 −0.74328978 RPL8 0.644367573 NLRC5 −0.474588382 TMEM126B −0.757728329 PGM2L1 0.643897977 SULT1A2 −0.482875287 GTF3C6 −0.795216188 FKBP1A −0.492172182 JAM3 −0.497945832 FCGR2B −0.514251626 CLEC12B −0.515195232 TRPC4AP −0.519258529 C11orf82 −0.521156625 PTK2B −0.524676726 GPR65 −0.525797342 KLF5 −0.527857833 PKM2 −0.539118323 SAP3L −0.539171373 SULT1A3 −0.547825718 ANXA2P1 −0.548762819 NFKBIB −0.558246324 GDI1 −0.561865494 PSRC1 −0.564178565 HHEX −0.583227669 DIP2B −0.594517957 WWP2 −0.614284312 LOC42221 −0.626577759 SIGLEC7 −0.627915225 LOC1124692 −0.6312228 LILRA1 −0.634928539 MEF2A −0.639317827 HSH2D −0.649436192 CTSC −0.655139391 BIN2 −0.655173425 LSP1 −0.668495558 TNFSF13 −0.67161967 EFCAB2 −0.682346884 LOC113251 −0.688489257 ILK −0.693325115 HIST1H2AD −0.695734597 LOC648733 −0.696389547 C1orf58 −0.712867866 KDM1B −0.718128564 AQP12A −0.724567526 LOC65275 −0.73677314 ITGAX −0.744397547 IRF2 −0.769235155 AFF1 −0.784337538

TABLE 1.1 Top 50 genes of set 1 with absolute value of weights closer to 1 (highest weight from 0.818423753 to 0.935783593): CD3D UXT  RPS4X LOC283412 LOC127295 LOC42694 SKAP1 LOC72882 LOC645173 RPL23A LOC646942 LOC646294 LOC728428 LOC44737 LOC7329 LOC391833 RPS3 RPL36 LOC1127993 LOC73187 LOC72831 LOC653162 LOC729679 LOC441246 LOC387841 C13orf15 LOC728576 EIF3K EEF1B2 LCK LOC39345 RPL4 LOC1132742 EIF3H CD27 RPS15 LOC649447 LOC1131713 LOC286444 LOC729789 RPL1A CD6 LOC646766 C17orf45 CUTA EIF3F LOC642741 LOC388339 RPS14 LOC11398

TABLE 1.2 Top 50 genes of set 2 with absolute value of weights closer to 1 (highest weight from 0.711353632 to 0.899346773): CUTL1 MAST3 STK4 KIAA247 MYH9 RAPGEF2    ARAP3RAB11FIP1 WBP2 GNAI2 MTMR3 GTF3C6 CBL UBE4BIGF2R    YPEL3 SETD1B PIK3CD RASSF2 KDM6B TP53INP2    NUAK2 PAK2 MYO9B NDE1 TMEM126B IRS2 PHF2 MAP2K4    CAMK1D NUDCD2 CDC2L6 ETFB ASAP1 TSC22D3    TLN1 ANXA11 EP3 ROD1 RXRA RASSF5 PELI2 SEMA4D    LOC113932 PPM1A CREBBP LAPTM5 CABIN1 PLCB2 WNK1

TABLE 1.3 Top 50 genes of set 3 with absolute value of weights closer to 1 (highest weight from 0.717268353 to 0.952217397) LOC44926  ITM2B HOXC6 LOC392288 YIPF4 RBMS1 USP6 KIAA133 LOC642567 EVI2B UBE2W DDX3X UBE2D1 HIAT1 TTRAPLOC44525 C18orf32 LOC1132888 ROCK1 LOC64798 FAM91A2 SENP6 LOC732229 CEP63 ATG3 LOC1128269 PLAGL1 MBD2 EXOC8MRRF LOC113377 POTE2 C8orf33 LOC38953 CPEB3 C6orf211 LOC1128533 LOC648863 STX7 SEPT14 LOC4493 LOC442319 NCRNA81 CLEC7A CSNK1A1L LOC643896 P74P LOC4948 GABARAPL2 FCGR3A

TABLE 1.4 Top 50 genes of set 4 with absolute value of weights closer to 1 (highest weight from 0.53314 to 0.982476) SDPR PDE5A PTGS1 CTDSPL    CTTN ALOX12 MPL DNM3 C1orf47 C7orf41 C5orf4 RAB27B CXorf2 GRAP2CDC14B DAB2 TAL1 NCALD ITGB5 GUCY1A3 FERMT3 TSC22D1 LIMS1 SLC8A3 ABCC3HOMER2 NAT8BFBLN1 ARHGAP21 C21orf7 C15orf52 CABP5 ENDOD1 SOCS4 C15orf26      PVALB SLC24A3     HGD ZNF185 CA2 CXCL5 GRB14 VWF  DKFZp686I15217 NDUFS1 GRASPRGS18 C16orf68    MGC135      LOC64926 HIST1H2AE

In certain embodiments, normalized gene expression values of the signature genes in Table 1 can be used as is, thus without weighting, for the classification of ASD vs non-ASD. In certain embodiments, using Boosting (see Scoring and Classification methods) three lists of genes were identified with the smallest number of elements that classified subjects with accuracy of at least 70%, at least 75%, and at least 80%. Sets with 20, 25 and 30 features that can produce at least 70%, at least 75%, and at least 80% correct classification include but are not limited to those shown in Table 2 below. In certain embodiments, adjusting the weights of the genes based on the age of the subject is the most important single parameter for improving accuracy of ASD classification

TABLE 2 Minimum # Accuracy % of features Gene list + AGE 80% 30 “AGE” AK3 LOC100132510 ARID4A CMTM4 KIAA1430 LOC441013 MAL SETD1B AKR1C3 ATXN7L3B PARP15 AP2S1 CA2 PAN3 MTMR3 TOP1P2 UHRF2 LOC92755 EPOR MED31 LOC389286 LOC646836 MSRB3 GPR65 SMPD1 GPX4 LOC100133770 PRKCB LOC100129424 75% 25 “AGE” FCGR3A LOC389342 IGF2R ARAP3 PDE5A MPL CUTL1 LOC642567 SDPR PTGS1 MIR1974 MAP1LC3A LILRA3 LOC100133875 SPI1 LOC653737 IRS2 MAST3 NCF1B STK40 KIAA0247 LOC648863 CTDSPL NCALD 70% 20 “AGE” IGF2R ARAP3 FCGR3A LOC389342 LOC648863 SPI1 LOC642567 CUTL1 PDE5A ASAP1 KIAA0247 MAP1LC3A ZNF185 IRS2 MTMR3 LOC100132510 IMPA2 NCALD MPL

The gene-networks signature matrix (GNSM) is a matrix containing weights and features calculated from gene-to-gene interaction patterns. These interaction patterns can also be calculated based on the relationship or state of a gene with non-genomic features.

The multi-modal signature matrix (MMSM), which is a matrix containing the quantification of non-genomic features obtained by clinical, behavioral, anatomical and functional measurements. In certain embodiments, the MMSM includes, but is not limited to, age, GeoPref Test²⁸, MRI/fMRI/DTI, and ADOS test.^(29,30) Scores from questionnaires are also included in the MMSM for instance the CSBS test.³¹

The collateral features signature matrix (CFSM), which is a matrix containing any features that are not related to the subject under study. In certain embodiments, the CFSM includes, but is not limited to, analytes in maternal blood during pregnancy, sibling with autism, maternal genomic signature or preconditions, and adverse pre- or perinatal events.

In certain embodiments, the invention provides the use of the weighted gene signature matrix (WGSM) which is based for example on four sets of genes and gene-weights (Weight Sets 1-4, see Table 1) that predict autism with high accuracy. In some exemplary embodiments, the WGSM includes a total of 762 genes as listed above (see Table 1), 2 or more genes arranged in any number of sets can be included, as well. It is to be understood that the exact number of genes used in the method can vary as well as the type of genes based on the model derived from the Autism Reference Database.

In certain embodiments, the WGSM technology of the invention comprises the following steps:

Step 1: Collection of quality blood leukocyte samples and extraction of RNA from leukocytes. Blood leukocytes are collected from a newborn, infant, toddler or young child as part of a general pediatric screening procedure or as a diagnostic test for those at high risk for autism (such as younger siblings of an autistic child) or suspected to have autism. Temperature and history are taken and documented prior blood sample collection. Samples are collected if the child has no fever, cold, flu, infections or other illnesses or use of medications for illnesses 72 hours prior blood-draw. If a child has a fever, cold, etc, then blood samples should be collected no sooner than a week after the illness is over.

Four to six ml of blood is collected into EDTA-coated tubes. Leukocytes are captured and stabilized immediately (for instance via a LEUOLOCK filter, Ambion, Austin, Tex., USA) and placed in a −20 degree freezer for later processing.

mRNA is extracted from leukocytes according to standard practices. For example, if LEUKOLOCK disks are used, then they are freed from RNAlater and Tri-reagent is used to flush out the captured lymphocytes and lyse the cells. RNA is subsequently precipitated with ethanol and purified though washing and cartridge-based steps. The quality of mRNA samples is determined with RNA Integrity Number (RIN) assays and only values of 7.0 or greater are considered acceptable for use in the next steps. Quantification of RNA is performed using, as an example, Nanodrop (Thermo Scientific, Wilmington, Del., USA).

Step 2: Determination of gene expression levels for genes used in the Weighted Gene Test of Autism. Whole-genome gene expression levels are obtained by using either a microarray-based platform (such as Illumina HT-12 or equivalent) or next-generation sequencing. The analysis of gene expression levels can also be performed using a targeted approach based on custom microarrays, targeted sequencing or PCR-based amplification of the WGSM and/or gene-networks signature matrix (GNSM) genes (see below Gene Expression Profiling).

Whichever method is used, however, it should provide high fidelity expression levels for each of the genes in the WGSM. This is achieved by using methods that interrogate the signal intensity and distribution of each probe/gene. For instance, a detection call p-value of 0.01 is used as the threshold to filter out probes/genes with expression levels of poor quality. For analyses performed on multiple subjects simultaneously, any probe/gene with no detectable levels in at least one subject is also eliminated. Once the final set of probes/genes with high fidelity expression levels is determined, the data is transformed (for instance with the “log 2” function) and normalized. The normalization step is helpful in order to obtain informative and comparable expression levels to the weighted gene expression reference database.

In certain embodiments, the weighted gene feature test of autism (WGFTA) technology utilizes the simultaneous analysis of at least 20, 40, 80, 150 or more subjects (recruited and processed with similar criteria of the reference dataset discussed below) for independent normalization. In the case of fewer subjects, these subjects can be added to the reference database prior to normalization. Normalization can then be performed using for instance the “quantile” method.

At the conclusion of Step 2, the normalized gene expression value (NGEV) for an individual subject or patient has been determined for each gene in the WGM. In some embodiments, one or more NGEVs are used to classify genes for use in the methods of the invention without further using a gene-specific weight. In certain embodiments, the NGEVs are used with MMSM and/or CFSM values. In alternative embodiments, the NGEVs are used without MSSM and/or CFSM values.

Step 3: The procedure in the weighted gene feature test of autism (WGFTA) involves application of the gene-specific weights from the weighted gene signature matrix to the NGEV in each child. For each gene in the WGSM, its NGEV is multiplied by that gene's gene-specific weight (for example, see Table 1). The resultant value for each gene is the weighted gene expression level. In certain embodiments, the genes in the representative example Weights Sets 1-4 constitute the genes in the WGSM and used in the WGFTA.

The weighted gene expression levels in a subject's (or patient's) sample can be further processed to reduce dimensionality using methods such as principal component analysis (PCA) or eigenvalues or multi-dimensional scaling (MDS). This step reduces computation time, data noise and increases power in the subsequent analysis steps, while it preserves the biological information useful for the classification. If computation power, time and data noise is not an obstacle, then the weighted gene expression level data in each subject or patient can be used as is in the next step.

Step 4: the second procedure in the weighted gene feature test of autism (WGFTA) is the comparison of weighted gene expression levels to a unique autism and control weighted gene expression reference database. The subject or patient's set of weighted gene expression levels is compared to the specific multidimensional weighted gene expression reference database to establish a score for autism risk and/or a class identity (ASD, non ASD). Two different scoring or CLASS identity methods are applied (see below).

In certain embodiments, the performance of the invention includes: the prediction accuracy of the weighted reference database, the ROC curves with estimated AUC, Accuracy, Specificity, Sensitivity and the matrix of weights for the identified gene-sets. See FIGS. 4C and 4D (Logistic regression analysis and classification outcome of the weighted reference database) and Table 1.

Scoring/CLASS Identity Methods

In certain embodiments, the following scoring methods are used. However, any available scoring methods, now known or later developed, are encompassed within the scope of the invention.

In certain embodiments, methods use boosted classification trees to build the screening, diagnostic and prognostic classifiers, with or without the use of modules to classify the genes. This classification regime is divided into two main components. First, the underlying classification algorithm is a classification tree. Second, boosting is applied to this baseline classifier to increase the prediction strength. The resulting learning algorithm retains the strengths of the baseline classifier while improving the overall predictive capability. In particular embodiments, there are two classes, ASD and non-ASD; the classes are represented symbolically by +1 and −1. The training dataset consists of labeled cases (x₁, y₁), (x₂, y₂), . . . , (x_(N), y_(N)). Here, y_(i) is a class label and x_(i) is vector of variables or features measured for the i-th individual. A classifier is represented by a function C(x) whose input is a vector x in the feature space and whose output is one of the class labels.

In the first component, namely classification trees, the underlying learning algorithm used is a decision tree for classification. Any classifier can be represented by a partition of the feature space into disjoint regions R₁, R₂, . . . , R_(k) and associated labels c₁, c₂, . . . , c_(k). The class of a new, unlabeled case is predicted by locating the region into which the feature coordinates of the case falls and reading off the class label for that region. In a decision tree, this partition is represented by the leaf nodes in a binary tree (see FIG. 15). Starting at the root of the tree, each node represents a subdivision of a region of the feature space by splitting it on one of the variables. The feature space is thus recursively divided into increasingly finer sub-regions. The “leaf” nodes at the bottom of the tree are affixed with class labels. The best partition for classification is learned from the data: for a given node, the variable from the full feature set and the threshold value for that variable that best separate the data into its constituent classes is selected, producing two child nodes. The selection is based on maximizing some measure of fitness of the resulting classifier, such as the information gain. The process is repeated for each node until a halting criterion is reached, such as when all of the training data points in a given sub-region are of the same class.

Then, in the second component, namely boosting, the classification tree is improved using a boosting algorithm (such as AdaBoost). This algorithm works by iteratively fitting a baseline classifier and using its performance on the training data to re-weight the importance of each point in subsequent fits (see FIG. 16). Initially, each of the data points is given equal weight. After fitting the classifier, the error rate on the training data is used to produce a weight α associated with the classifier. The weights of the data points are then updated: the weights of misclassified points are increased while correctly labeled points are de-emphasized. This forces the next classifier to pay more attention to cases where errors were previously made. The process is repeated using the re-weighted observations in the next iteration; it halts when the test error—computed from a test data set or via cross validation—has stabilized, or when a fixed number of iterations has been reached. Formally, the algorithm proceeds as follows. Let w_(i) be the weight of the i-th training point, i=1, . . . , N. Initialize the weights as w_(i)=1/N. For j=1, . . . J, do the following:

-   -   1. Fit the classifier C_(j)(x) to the weighted data set.     -   2. Compute the weighted training error rate e_(j)=Σ_(i=1)         ^(N)w_(i)I(y_(i)≠C_(j)(x_(i)))/Σ_(i=1) ^(N)w_(i).     -   3. Compute the weight α_(j)=ln((1−e_(j))/e_(j)).     -   4. For each i, update the weights according to w_(i)←w_(i)×e^(α)         ^(i) ^(I(y) ^(i) ^(≠C) ^(j) ^((x) ^(i) ⁾⁾.

The result is a sequence (C₁(x), α₁), (C₂(x), α₂), . . . , (C_(J)(x), α_(J)) of classifiers and associated weights. The sequence is combined into a final classifier by taking the sign of a weighted sum of the sequence: C(x)=sign(Σ_(j=1) ^(J)α_(j)C_(j)(x)).

In other embodiments, an alternative to the tree-based classifier can be used such as distance-based methods that utilize distances in the feature space in order to predict the class labels. The procedure can quantify the extent to which a given set of features conforms to each of the classes, and predicts the label of the class with the highest concordance. For each class, the mean vector μ and covariance matrix Σ of the feature distribution is estimated using the sample mean and sample covariance matrix. Then, for a given point x in the feature space, the Mahalanobis distance between the x and the mean d=((x−μ)^(T)Σ⁻¹(x−μ)^(1/2) is computed. The predicted label for x is the label corresponding to the class that minimizes this distance. The performance of the resulting classifier can then be improved by using it as the baseline classifier in the boosting procedure outlined above.

With multiple feature sets, the model detailed here can be fit using a wide range of features for prediction. In some instances, only certain types of features may be available at the time of prediction. For example, only gene expression signatures and age might have been observed for a particular patient. The model can be fit using various combinations of feature modalities from the MMSM and CFSM as well as GNSM. The result is a suite of classifiers, each one suited to a different configuration of feature types. This yields a classification procedure that can be utilized for a range of patient data availabilities and thus is robustly useful in the applied setting.

Performance of the WGSM was tested with several algorithms including, but not limited to, Random Forest-, Neural Network-, Support Vector Machine-, Boosting- and Logistic Regression-based methods and independently validated on a second dataset of autism and non-autism subjects. This testing showed high-accuracy in diagnostic classification of autism (80% or greater classification accuracy), thus confirming: 1) the efficacy and specificity of a unique pattern of gene weights, 2) the relevance, sensitivity and specificity of the identified four sets of genes, and 3) the reliability of the multi-dimensional weighted reference of autism and control.

Score calculation and class prediction are generated by the computer-based algorithms selected to test the WGSM on the new subject(s) (see previous paragraphs). A comparison of matrices is performed by using distance-based classification between the new subject(s) matrices and the referenced matrices from both the ASD and control subjects.

Gene Expression Profiling

The invention enables the use of both genome-wide and gene targeted approaches to quantify gene expression levels of peripheral blood leukocyte of a test subject. As used herein, the Genome-wide approaches include, but are not limited to, the use of microarray-based platforms and next-generation sequencing. Expression levels of the genes belonging to the WGSM are extracted after standard normalization, transformation and filtering steps (see Methods in the Examples below). As used herein, the Gene-targeted approaches include, but are not limited to, microarray-based platforms or PCR-based amplification.

With the targeted approaches only the expression levels of the genes belonging to the WGSM are determined. The use of whole-genome microarrays requires an a priori construction of a gene-library or the use of a gene-capturing method. Alternatively, the targeted approach via microarray-based platform is done by the construction of custom-designed gene expression microarrays containing only the genes from the four gene-sets with control and reference probes and replicated on the same platform to allow high reproducibility and testing of multiple patients. Gene expression levels are then calculated with the use of control probes, reference genes and/or experiments.

WGSM Features:

-   -   1) Signature gene composition: The provided example of the WGSM         includes 762 genes. However, any 2 or more genes can be assayed         on different platforms, array-based, sequencing based or         PCR-based.     -   2) Splice variants information of the genes within the WGSM is         also used.     -   3) Data redaction tools are also applied to the genes of the         WGSM.

In some embodiments, the invention provides that the WGSM can be used alone in the Weighted Gene Feature Tests of Autism (WGFTA) or in combination with one or more of the other matrices described above. In certain embodiments, the combination use of the WGSM with subject's age as Multi-Modal Signature Matrix (MMSM) is provided.

A major strength of the signature discovery was the recruitment of subjects using a general, naturalistic population screening approach. This approach allowed the unbiased, prospective recruitment and unique study of autism and contrast patients as they occur in the community pediatric clinics. To maximize the number of ASD and control subjects for the signature discovery, a slight age difference is tolerated in the two subject distributions and age is included as a predictor in the classification analysis. The impact of all predictors was then assessed in the classification of the subjects by logistic regression with binomial distribution. The output of these analyses is provided as follows:

-   -   a) Analysis using age as the only predictor of diagnosis showed         a very small ODDS ratio of 1.07 towards the ASD CLASS.     -   b) Analysis using the Weights Sets 1-4 as predictor singularly         showed ODDS ratios (9577.88, 17423.52, 4.16e-05 and 3716.94         respectively).     -   c) Analysis using all predictors together (Weights Sets 1-4 and         age) showed again very large ODDS ratios for the Weights Sets         1-4 predictors (1.73e+06, 1.46e+05, 5.31e-03 and 6.235152e+01         respectively) and an ODDS ratio close to 1 for age (1.089).         Using different algorithms, classification performance improved         on average by 3-4% (see Table 3).

TABLE 3 Classification performance using different algorithms with and without age as predictor % Accuracy without AGE % Accuracy with AGE Algorithm discovery replication discovery replication Name set set set set glmnet 78 72 82 75 mlp 78 72 83 69 cforest 87 70 91 72 svm radial 81 68 87 70 random forest 100 70 100 67 qvnnet 84 65 84 71

It is known that the transformation from ODDS values to probability is a monotonic transformation following an exponential curve. An ODDS ratio of 1 indicates a 0.5 probability to fall into either CLASS, in this case ASD and non-ASD. An ODDS value tending to infinity or zero indicates a very high or very low probability, respectively, to be classified ASD. Therefore, it is demonstrated that although age effects are present in this study, they are very small considering the effects of the gene expression signature predictors (Weights Sets 1-4 in Table 1). Moreover, this effect is empirically quantified by classification of both discovery and replication subjects with and without age as a predictor. It was found that classification accuracy increased by about 3-4% or more when age was included as a predictor in the analysis, and so in certain embodiments, the invention uses age as a predictor in screening, diagnostic and prognostic signatures of ASD, as shown in one Example 3 below.

Similarly, additional predictors from the MMSM which includes non-genomic quantifiable features obtained by clinical, behavioral, anatomical and functional measurements. In certain embodiments, clinical features are scores on the ADOS, Mullen, Vineland, and any other diagnostic and psychometric test instruments. In certain embodiments, neurobehavioral features are eye-tracking tests such as the GeoPreference Test of autism and exploration tests. In certain embodiments, anatomical features are MRI neuroanatomical measures including, but not limited to, global and regional gray or white matter volumes, cortical surface areas or thickness and cortical gyrification as determined by methods including, but not limited to, voxel-based, statistical mapping-based and surface or structure reconstruction based methods (e.g., temporal grey matter volumes, and DTI measures including tract fractional anisotropy (FA) and volume and gyral patterns of cortical tract projections).

In certain embodiments, the functional features are fMRI measures including, but not limited to, activation, psychophysiological (PPI), dynamic causal modeling, unsupervised classification information maps and values. Similarly features from the GNSM and CFSM are used with or without the WGSM as predictors in the classification and prognostic analyses.

Therefore, the invention in some embodiments utilizes a test based on a specific pattern of specific-gene weights in a person that are involved in governing cell cycle, DNA damage response, apoptosis, protein folding, translation, cell adhesion and immune/inflammation, signal transduction ESR1-nuclear pathway, transcription-mRNA processing, cell cycle meiosis, cell cycle G2-M, cell cycle mitosis, cytoskeleton-spindle microtubule, and cytoskeleton-cytoplasmic microtubule functions. The WGFTA provided by the invention requires high quality molecular components, including RNA, genomic DNA, cellular and serum proteins, and small molecule analytes, that are extracted by clinically standard methods from blood and other tissues collected using clinically routine methods from ages of birth to 1 year, 1 year to 2 years, 2 years to 3 years and 3 years to 4 years. The present invention provides that the DNA and/or mRNA can be collected in many ways and/or isolated or purified directly from a biological tissue or cell sample, including but not limited to tears, saliva, mucous, buccal swab, whole blood, serum, plasma, cerebrospinal fluid, urine, and the like, or cells including, but not limited to fibroblasts, iPS cells, neuroprogenitor cells derived from iPS cells, and neurons derived from iPS cells, etc. A biological sample could also be obtained from specific cells or tissue, or from any secretions or exudate. In certain embodiments, the biological sample is a biological fluid obtained from peripheral blood. In certain embodiments, DNA is isolated or purified from peripheral blood nuclear cells (PBMCs) derived from fresh blood. Techniques for purification of biomolecules from samples such as cells, tissues, or biological fluid are well known in the art. The technique chosen may vary with the tissue or sample being examined, but it is well within the skill of the art to match the appropriate purification procedure with the test sample source.

In some embodiments, the WGFTA of the invention uses any one of several known and state-of-the-art whole genome RNA-based/gene expression assay (such as RNA sequencing, custom gene expression arrays, PCR-based assays, state-of-the-field whole genome microarrays or genome sequencing) that give accurate expression levels. In some embodiments, the WGFTA is based on gene sets such as, for example the four sets: Weights Set 1, Weights Set 2, Weights Set 3 and Weights Set 4 in Table 1. In certain embodiments, the WGFTS includes specific splice variants of the genes. In some embodiments, the Weighted Gene Matrix comprises genes in the WGFTA and their Gene-Specific Weights (see Table 1). Furthermore, in certain embodiments, the autism-critical weighted gene expression levels is the transformation of an individual's normalized expression levels of the genes in the weighted gene signature matrix by gene-wise multiplication of the gene-specific weights.

Depending upon the factors unique to each case and desired level of specificity and accuracy, any number of genes may be selected, for example, from those described in Table 1. In some embodiments, the genes are generally ranked according to relative importance based on the absolute value of the weight. In certain embodiments, the number of genes chosen includes at least 10, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750 or more genes, including intervening and greater numbers, within a selected gene set.

In certain embodiments, the invention of the WGFTA is (a) the application of the unique gene-specific weights to an individual's normalized gene expression values for those genes in order to derive that individual's autism-critical weighted gene expression levels; (b) the application of any subset of the unique Gene-Specific Weights derived from the effort in optimizing the classification performance of the Weighted Gene and Feature Test of Autism; (c) the modification of the Weights in the Weighted Gene Signature Matrix as the result of the optimization of the Autism and non-autism Weighted Gene Expression Reference Database from which the WGM is derived; (d) the comparison of an individual's Autism-Critical Weighted Gene Expression Levels to the Autism and not-autism Weighted Gene Expression Reference Database; and (e) the development and use of any RNA-based assay that uses the Weighted Gene Signature Matrix to test risk for autism.

In some embodiments, the invention also provides that the development and use of any RNA-based gene expression data combine with MMSM measures (for example anatomical and/or functional brain measurements) to screen for autism risk or diagnostically classify autism and other developmental disorders. For example, in certain embodiments, age is considered in conjunction with a subject's gene expression levels and as a predictor (for example see Scoring/CLASS identity methods above) in adjusting and/or improving screening for autism risk, autism diagnostic classification and prognosis analysis, and the WGFTA is based on the comparison of an autistic subject(s) to a non-autistic subject(s). Further, the use of the GeoPreference test score, CSBS (communication and symbolic behavior scales) test scores and genomic DNA (CNV, SNV, indel) markers in combination with expression signatures (for example in one embodiment of the method described in Scoring/CLASS identity methods above) increase the WGFTA performance and improve classification of autism and other developmental and neuropsychiatric disorders.

In some embodiments, the autism and non-autism reference database provided by the invention comprises the collection of Gene Expression Levels, Weights and all non-genomic features already described that were uniquely derived from the fully clinically characterized and diagnostically confirmed infants, toddlers and young children with autism, typically developing (TD), and non-autism non-TD subjects.

Therefore, the weighted gene and features tests of autism (WGFTA) provided by the invention can in some embodiments be used in pediatric population screens for risk of autism and in clinical follow-up diagnostic and prognostic evaluations of newborns, infants, toddlers, and young children who are suspected to be at risk for autism. Some attributes of the invention are based on analyses of in vivo functional genomic abnormalities in mRNA expression from blood leukocytes as they relate to the measures of brain and cerebral size and to mRNA expression patterns in typically developing controls. Thus, in certain embodiments the invention is based on direct experimental knowledge of the functional genomic defects and the resulting brain size relationships that are disrupted in autistic toddlers as compared to control subjects.

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EXAMPLES Example 1 Disrupted Gene Networks in Autistic Toddlers Underlie Early Brain Maldevelopment and Provide Accurate Classification

Genetic mechanisms underlying abnormal early neural development in toddlers with Autism Spectrum Disorder (ASD) remain unknown, and no genetic or functional genomic signatures exist to detect risk for ASD during this period. The objective in this example was to identify functional genomic abnormalities underlying neural development and risk signatures in ASD.

A general naturalistic population screening approach was used to allow prospective, unbiased recruitment and study of ASD and control (typically developing and contrast) toddlers from community pediatric clinics. Whole-genome leukocyte expression and MRI-based neuroanatomic measures were analyzed in a discovery sample of 142 males ages 1-4 years. Co-expression analyses were applied to identify gene modules associated with variations in neuroanatomic measures and a candidate genomic signature of ASD. Class comparison and network analyses were used to identify dysregulated genes and networks in ASD toddlers. Results were compared to a Replication sample of 73 toddlers.

Correlations of gene expression profiles with deviation in neuroanatomic measures from normative values for age were performed in ASD and control toddlers. Classification performance was tested using logistic regression and ROC analysis. Cell cycle and protein folding gene networks were strongly correlated in control toddlers with brain size, cortical surface area, and cerebral gray and white matter, but weakly correlated in ASD. ASD toddlers instead displayed correlations with an abnormal array of different gene networks including immune/inflammation, cell adhesion, and translation. DNA-damage response and mitogenic signaling were the most similarly dysregulated pathways in both Discovery and Replication samples. A genomic signature enriched in immune/inflammation and translation genes displayed 75-82% classification accuracy.

The functional genetic pathology that underlies early brain maldevelopment in ASD involves the disruption of processes governing neuron number and synapse formation and abnormal induction of collateral gene networks. The orderly correlation between degree of gene network dysregulation and brain size, suggest there may be a common set of underlying abnormal genetic pathways in a large percentage of ASD toddlers. Knowledge of these will facilitate discovery of early biomarkers leading to earlier treatment and common biological targets for bio-therapeutic intervention in a majority of affected individuals.

Significant advances have previously been made in understanding the genetic¹⁻³ and neural bases⁴⁻⁶ of autism spectrum disorder (ASD). However, establishing links between these two fundamental biological domains in ASD has yet to occur. Clinical macrocephaly at young ages occurs in an estimated 12% to 37% of patients, but a subgroup has small brain size. However, genetic explanations for this wide variation remain uncertain. Moreover, genetic signatures of risk for ASD in infants and toddlers in the general pediatric clinic have not yet been found.

A long-theorized brain-gene link is supported by new ASD postmortem evidence, at least in ASD with brain enlargement. The theory⁷ is that early brain overgrowth, which occurs in the majority of ASD cases^(4,5,8-12), may result from overabundance of neurons due to prenatal dysregulation of processes that govern neurogenesis, such as cell cycle, and/or apoptosis. A recent postmortem study discovered overabundance of neurons in prefrontal cortex, a region that contributes to autistic symptoms, in ASD children with brain enlargement⁶, and a second postmortem study reported abnormal gene expression in cell cycle and apoptosis pathways also in prefrontal cortex in ASD male children². Gene pathways identified in the latter ASD postmortem study are consistent with those identified by CNV pathway enrichment analyses in living ASD patients¹³. A complementary theory is that synapse abnormalities may also be involved in ASD¹⁴, but how this may relate to early brain growth variation is unknown. Because direct analyses of brain-genome relationships during early development have never been done in ASD, it remains unknown whether genetic dysregulation of cell cycle, apoptosis and/or synapse processes underlie variation in brain growth and size in the majority of ASD toddlers. Since neuron number and synapse formation and function are developmentally foundational and drive brain size, common pathways leading to ASD may involve their dysregulation.

A novel study of genomic-brain relationships in vivo was performed in ASD and control toddlers. Unique to this study was that all toddlers came from a general naturalistic population screening approach that allows for the unbiased, prospective recruitment and study of ASD, typically developing (TD) and contrast toddlers as they occur in community pediatric clinics. Unbiased data-driven bioinformatics methods were used to discover functional genomic abnormalities that are correlated with brain anatomy at the age of clinical onset in ASD and distinguish them from TD and contrast toddlers. With this naturalist general population approach, it is also able to test whether some functional genomic abnormalities might also provide candidate diagnostic signatures of risk for ASD at very young ages.

Methods

Subjects, Tracking and Clinical Measures

Participants were 215 males ages 1-4 years. 147 toddlers were in a Discovery sample (N=91 ASD, 56 control) and 73 (N=44 ASD, 29 control) in a Replication sample. Toddlers were recruited via the 1-Year Well-Baby Check-Up Approach from community pediatric clinics¹⁵ (see Methods in Example 2) that enables a general naturalistic population screening approach for prospective study of ASD, typically developing subjects and contrast patients. In this approach, parents of toddlers completed a broadband developmental screen at their pediatrician's office, and toddlers were referred, evaluated and tracked over time. This provided an unbiased recruitment of toddlers representing a wide range and variety of ability and disability. Blood samples for gene expression, DNA analysis and MRI brain scans were collected from a subset of subjects at time of referral, regardless of referral reason, and before final diagnostic evaluations. Every subject was evaluated using multiple tests including the appropriate module of the Autism Diagnostic Observation Schedule (ADOS)^(16,17) and the Mullen Scales of Early Learning¹⁸. Parents were interviewed with the Vineland Adaptive Behavior Scales¹⁹ and a medical history interview. Subjects younger than 3-years of age at the time of blood draw were longitudinally diagnostically and psychometrically re-evaluated every 6-12 months until their 3^(rd) birthday, when a final diagnosis was given. Subjects were divided into two study groups: ASD and control. The control group was comprised of typically developing (TD) and contrast (e.g., language, global developmental or motor delay) toddlers (Table 4).

TABLE 4 Summary of subject characteristics and clinical information Discovery Replication Subjects Characteristics ASD TD Contrast ASD TD Contrast Age in years - Mean (SD) 2.3 (0.7) 2.0 (0.9) 1.5 (0.6) 2.3 (0.8) 1.6 (0.7)  1.2 (0.2) AD 77 31 TD 41 25 PDD-NOS 10 13 Language Delayed ‡ 9 2 Globally Developmentally Delayed 

1 Radiological abnormality 1 1 Premature birth, testing normally 

2 Socially Emotionally Delayed 

1 Drug Exposure 

1 Ethnicity Hispanic 24 5 2 13 3 1 Race Caucasian 44 29 9 23 17 3 Asian 4 2 1 2 African-American 1 1 1 1 Mixed 13 4 1 6 3 Indian 1 Unknown 1 Subjects Clinical Information ASD TD Contrast ASD TD Contrast Mullen Scales of Early Learning (T-Scores) - Mean (SD) Visual Reception 39.7 (11.0) 59.0 (10.3) 48.1 (9.0)  40.6 (13.6) 51.6 (10.2) 44.3 (4.5) Fine Motor 37.3 (12.2) 55.9 (9.1)  55.8 (8.4)  40.1 (16.0) 57.5 (8.5)  55.7 (2.9) Receptive Language 29.1 (12.0) 52.4 (8.3)  46.9 (8.5)  31.6 (16.1) 50.7 (10.2) 36.7 (4.9) Expressive Language 29.1 (11.4) 53.7 (9.5)  46.3 (7.9)  31.4 (16.4) 52.0 (8.6)  41.0 (2.6) ADOS T Social Affect Total, Modules 1 and 2 Communication + Social Interaction Total - Mean (SD) ADOS CoSo/SA Score * 15.0 (3.9)  2.1 (1.7) 0.6 (1.1) 12.8 (4.8)  2.4 (2.2)  5.0 (5.0) ADOS RRB Score 4.1 (1.9) 0.3 (0.5) 4.1 (4.7) 2.5 (1.6) 0.3 (0.4)  0.7 (1.2) ADOS Total Score 19.1 (4.7)  2.4 (1.9) 2.1 (2.5) 15.3 (5.4)  2.6 (2.3)  5.7 (6.0) Early Learning Composite 71.0 (16.2) 110.5 (12.4)  98.7 (11.4) 76.1 (21.6) 106.0 (12.9)  89.3 (6.7) Vineland scores (VABS) † 82.2 (9.4)  101.6 (9.3)  92.4 (7.6)  83.6 (14.1) 100.8 (7.3)  95.0 (1.0) ‡ >1 standard deviation below expected values on the language subtests on the Mullen

 >1 standard deviation below expected values on 3 or more of the subtests of the Mullen and the overall developmental quotient was >1 standard deviation below expected values (i.e., <85)

  <than 37 weeks gestation

 Diagnosis of social emotional delay #Z,53 Mother with drugs exposure during pregnancy * Replication: 32% of ASD population had ADOS T, 48% had ADOS 1, and 20% had ADOS 2 Discovery: 64% of ASD population had ADOST, 31% had ADOS 1, and 5% had ADOS 2 † Adaptive Behavioral Scales Adaptive Behavior Composite Score Blood Sample Collection and Processing

Leukocytes were captured using LEUKOLOCK filters (Ambion, Austin, Tex.) from four-to-six ml of blood (see Methods in Example 2) for Discovery and Replication samples. RNA samples in the Discovery set were tested on the Illumina Human-HT12_v.4 platform, while the Illumina WG-6 platform was used for the Replication set. Five low-quality arrays were identified and excluded from statistical analyses (see Methods in EXAMPLE 2). Final samples were 87 ASD and 55 control Discovery toddlers and 44 ASD and 29 control Replication toddlers (Table 4).

MRI Scanning and Neuroanatomical Measurement

MRI data were obtained during natural sleep from Discovery toddlers (65 ASDs, 38 controls) whose parents consented to scanning. Twelve neuroanatomic measurements were obtained using a semi-automated pipeline integrating modified features of FSL and BrainVisa (fmrib.ox.ac.uk/fsl/; brainvisa.info), and included total brain volume, left and right cerebral gray and white matter volumes, left and right cerebral cortical surface areas, left and right cerebellar gray and white matter volumes, and brainstem volume (See Methods in EXAMPLE 2).

Statistical and Bioinformatic Analyses

Statistical analyses were performed on normalized and filtered expression data. Effects of age on neuroanatomic measures were removed via Generalized Additive Models (GAM-R package v1.06.2)²⁰.

Co-expression analysis (WGCNA) was used to identify gene modules across all Discovery subjects and within each study group separately (See Methods in EXAMPLE 2). WGCNA analysis, Pearson and Spearman correlations were used to identify associations between gene expression patterns and neuroanatomy across all Discovery toddlers. Gene Significance (gene expression level to phenotype correlation) and Module Membership (gene connectivity within each module) were also computed using WGCNA (See Methods in Example 2). Class comparison analyses were performed using a random variance model with 10,000 univariate permutation tests in BRB-Array Tools (linus.nci.nih.gov/BRB-ArrayTools.html). MetaCore software was used for pathway enrichment analyses. Hyper-geometric probability (Hyp. P) was used to test the significance of Venn analyses versus random gene sets of equal size (See Methods in EXAMPLE 2). Differentially expressed (DE) genes from Discovery toddlers were used to identify a potential gene expression signature of ASD, Four DE modules were selected based on AUC performance in classification of Discovery toddlers using a logistic regression function (glmnet). CNVision was used to call copy number variations (CNVs) in misclassified ASD subjects as previously described.^(2,21)

Results

The majority of Discovery and Replication subjects were of Caucasian origin. Pearson's Chi-squared test showed no significant difference in race/ethnicity distribution between ASD and control (Discovery X²=7.98, P=0.1569; Replication X²=7.19, P=0.2065).

Across ASD and control toddlers, age-corrected MRI total brain volume (TBV) measures followed a normal distribution with no statistically significant difference (FIG. 1A, P=0.645), as well as for the other measures.

After filtering across all Discovery subjects, 12208 gene probes were used for downstream analyses.

Different Gene-Networks Underlie Variation of Neuroanatomic Measures in ASD and Control Groups

WGCNA Across Combined ASD and Control Groups

Unsupervised co-expression analysis using WGCNA identified 22 modules of co-expressed genes (see FIG. 5) with eigengene values computed for each module and each ASD and control subject. Of these 22 modules, seven were consistently correlated with neuroanatomic measures across all subjects, including TBV, cerebral gray, cerebral white and cerebral cortical surface area (Table 5, FIG. 6) and displayed statistically significant enrichments (P<0.05, FDR<0.05; FIG. 1B). The greenyellow and grey60 gene modules displayed the strongest correlations with brain and cerebrum volumes across groups (Table 5) and all seven modules were associated with TBV measures. The greenyellow module displayed top enrichment in cell cycle functions, while protein folding genes were highest in the grey60 module (FIG. 1B, Table 8). Seven different gene modules were instead associated with diagnosis (see Table 26 in EXAMPLE 2) and Metacore analysis displayed no significant enrichment for the strongest correlated modules followed by cell cycle, translation and inflammation genes (see Table 26 in EXAMPLE 2).

TABLE 5 WGCNA association analysis (Pearson correlation) of module-eigengenes and age-corrected neuroanatomic measures in ASD and control toddlers together ASD/Control CB_GM CB_WM CBLL_GM CBLL_WM MODULE (L/R) (L/R) (L/R) (L/R) BS TBV Hemi_SA GreenYellow −0.32***/−0.33***  −0.3***/−0.28*** ns/ns ns/ns ns −0.31*** −0.29***/−0.3***  Grey60 −0.31***/−0.32*** −0.26**/−0.24** ns/ns ns/ns ns −0.3*** −0.26**/−0.27** Cyan 0.21*/0.2*  0.18*/0.17* ns/ns ns/ns ns 0.18* 0.14*/0.15* Turquoise 0.18*/0.19* 0.17*/0.17* ns/ns ns/ns ns 0.19* 0.16*/0.16* Yellow  −0.2*/−0.21* −0.17*/−0.17* ns/ns ns/ns ns −0.19* −0.18*/−0.18* LightGreen −0.19*/−0.21* −0.18*/−0.17* ns/ns ns/ns ns −0.21* −0.15*/−0.17* MidnightBlue  0.21*/0.22** ns/ns ns/ns ns/ns ns 0.21* 0.21*/0.21* Signif. codes relate also to FIG. 1B: p-value ***<0.001; **< 0.01; *<0.05 Signif L = Left, R = Right, CB = Cerebrum, CBLL = Cerebellum, GM = Gray Matter, WM = White Matter, TBV = Total Brain Volume, hemi = hemisphere, SA = Surface Area, BS = Brain Stem, ns = not significant

In control toddlers, only the cell cycle and protein folding module eigengenes (MEs) were strongly correlated with TBV and all cerebral measures (Tables 6 and 8). In contrast, ASD toddlers displayed correlations with several MEs, with the strongest being cell adhesion, inflammation and cytoskeleton regulation and the weakest being cell cycle, protein folding and transcription (Tables 6 and 8). Unlike control toddlers, cell cycle and protein folding MEs in ASD toddlers were not significantly correlated with cerebral white matter measures; instead, cerebral white matter volume was strongly correlated with cell adhesion and, to a lesser extent, inflammation and cytoskeleton regulation MEs (Table 6). Linear modeling of MEs with TBV variation (from small to big) displayed that cell cycle and protein folding genes have highest expression in normal small brains, while reduced to neutral effects are carried out by translation, cell adhesion, cytoskeleton and inflammation genes (FIG. 1C). Conversely, the combinatorial action of reduced activity of cell cycle and protein folding genes with a gain in expression of cell adhesion, cytoskeleton and inflammation seems to drive pathological brain enlargement in ASD (FIG. 1C).

TABLE 6 Pearson and Spearman correlations of module-eigengenes and age-corrected neuroanatomic measures in ASD and control toddlers separately Control CB_GM CB_WM Hemi_SA MODULE (L/R) (L/R) TBV (L/R) Top Network Grey60 −0.41**/−0.42** −0.49**/−0.48** −0.47**  −0.4*/−0.42** Protein folding_ER −0.47{circumflex over ( )}{circumflex over ( )}/−0.46{circumflex over ( )}{circumflex over ( )} −0.49{circumflex over ( )}{circumflex over ( )}/−0.49{circumflex over ( )}{circumflex over ( )} −0.53{circumflex over ( )}{circumflex over ( )}{circumflex over ( )} −0.4{circumflex over ( )}/−0.41{circumflex over ( )} and cytoplasm GreenYellow −0.43**/−0.44** −0.42**/−0.41*  −0.44** −0.39*/−0.43** Cell cycle_core −0.44{circumflex over ( )}{circumflex over ( )}/−0.45{circumflex over ( )}{circumflex over ( )} −0.37{circumflex over ( )}/−0.36{circumflex over ( )} −0.42{circumflex over ( )}{circumflex over ( )} −0.31{circumflex over ( )}/−0.35{circumflex over ( )}  ASD CB_GM CB_WM Hemi_SA MODULE (L/R) (L/R) TBV (L/R) Top Network MidnightBlue 0.35**/0.37** 0.29*/0.26* 0.35** 0.33**/0.34** Cell adhesion_integrin- 0.42{circumflex over ( )}{circumflex over ( )}{circumflex over ( )}/0.41{circumflex over ( )}{circumflex over ( )}{circumflex over ( )} 0.31{circumflex over ( )}/0.3{circumflex over ( )}  0.4{circumflex over ( )}{circumflex over ( )} 0.42{circumflex over ( )}{circumflex over ( )}{circumflex over ( )}/0.41{circumflex over ( )}{circumflex over ( )}{circumflex over ( )} mediated Turquoise 0.29*/0.29* ns/ns 0.29* 0.26*/0.25* Inflammation_interferon 0.39{circumflex over ( )}{circumflex over ( )}/0.38{circumflex over ( )}{circumflex over ( )} 0.29{circumflex over ( )}/0.29{circumflex over ( )} 0.39{circumflex over ( )}{circumflex over ( )} 0.35{circumflex over ( )}{circumflex over ( )}/0.31{circumflex over ( )} signaling Cyan 0.31*/0.31* ns/ns 0.27* 0.24*/0.25* Cytoskeleton_regulation 0.26{circumflex over ( )}/0.27{circumflex over ( )} 0.25{circumflex over ( )}/ns   0.28{circumflex over ( )} ns/ns and rearrangement Yellow −0.25*/−0.25* ns/ns ns ns/ns Translation_reulation −0.3{circumflex over ( )}/−0.3{circumflex over ( )} ns/ns −0.27{circumflex over ( )} −0.27{circumflex over ( )}/−0.25{circumflex over ( )} of initiation GreenYellow −025*/−026* ns/ns ns ns/ns Cell cycle_core −0.26{circumflex over ( )}/−0.3{circumflex over ( )}  ns/ns −0.27{circumflex over ( )} −0.28{circumflex over ( )}/−0.27{circumflex over ( )} Grey60 −0.25*/−025*  ns/ns ns ns/ns Protein folding_ER −0.29{circumflex over ( )}/−0.29{circumflex over ( )} ns/ns −0.28{circumflex over ( )} −0.28{circumflex over ( )}/−0.3{circumflex over ( )}  and cytoplasm Signif. codes: p-value Pearson ***<0.001; **<0.01; *<0.05; p-value Spearman {circumflex over ( )}{circumflex over ( )}{circumflex over ( )}<0.001; {circumflex over ( )}{circumflex over ( )}<0.01; {circumflex over ( )}<0.05 Correlations relate to FIG. 1C, D; L = Left, R = Right, CB = Cerebrum, GM = Gray Matter, WM = White Matter, TBV = Total Brain Volume, hemi = hemisphere, SA = Surface Area ns = not significant Network Patterns Alteration in ASD Vs. Control Groups

Calculation of the Gene Significance (GS) value for each module provides a measure of the impact of co-expressed genes on normal and pathologic brain size variation. Correlation analysis between GS and intra-modular Gene Connectivity (GC) revealed a major rearrangement of activity patterns across several gene networks (See Tables 21-23 for the genes with highest GS and GC). Twelve (12) of the 22 modules displayed a shift in pattern direction (negative to positive or not significant, and vice-versa) suggesting that for each of these 12 modules the impact of hub-genes on brain size variation was significantly altered in ASD compared to control (FIG. 7). Importantly, Cell cycle and Protein folding hub-genes displayed reduced GS values in ASD toddlers, while a substantial gain in GS was observed for hub-genes in the cytoskeleton, inflammation, cell adhesion and translation modules (FIG. 2). Similar analyses, assessing the specificity of a gene to a module (Module Membership, MM) in respect to its GS, supported the alterations in gene connectivity (FIGS. 2A-2C; See Tables 24 & 25 for the genes with highest MM).

WGCNA in ASD and Control Groups Separately

To further test for ASD-specific gene expression relationships to brain development, the same 12208 gene probes were analyzed by WGCNA within each study group (ASD, control) separately. Of 20 control-based co-expression modules, only 2 were significantly and strongly correlated to brain volume and cerebral measures (FIG. 8). As to the above across-groups analysis, these two modules were enriched in cell cycle and protein folding genes and displayed high GS values for normal TBV variation (FIG. 3; Table 9). Of 22 ASD-based co-expression modules, 11 were significantly correlated with one or more neuroanatomic measures (FIG. 9). Unlike control toddlers, these 11 modules had GS values consistent to the across-groups analysis and were enriched in multiple functional domains including immune, inflammation, cell adhesion, translation, and development (FIG. 3, Table 10).

DNA-Damage and Mitogenic Gene-Networks are Consistently Dysregulated in ASD Vs Control

Class comparison analyses between ASD and control toddlers found 2765 unique differentially expressed (DE) genes (see Table 16). Metacore enrichment displayed significant dysregulation for immune/inflammation response, DNA-damage/apoptosis and cell cycle regulation pathways as well as apoptosis, as the top Metacore process networks (Table 11). Pathway comparison between the Discovery and the Replication datasets indicated that DNA-damage response and mitogenic signaling were the most similarly and statistically significant dysregulated pathways in both samples (FIG. 4A, Table 12). At the gene level, 405 genes were commonly dysregulated and accounted primarily for networks involved in cell number regulation (FIG. 4B, Tables 13 and 17).

Venn analysis between the group-based gene modules associated with neuroanatomic measures and the 2765 DE genes, showed that 12.7% ( 37/290; Hyp. P=0.38) and 27.1% (786/2894; Hyp. P=1.8e-127) of the gene-modules were differentially expressed in control and ASD specific modules, respectively.

Key genes in the DNA-damage and mitogenic signaling categories were CDKs, CREB1, ATM, 14-3-3s, AKT, BCL2, PCNA, STAT1, PI3K, Beta-catenin, Caspases, NUMA1, NFBD1, PP2A, RADs and MAPKs (Tables 18 & 19).

Module-Based Classification Efficiently Distinguishes ASD from Control Toddlers

Co-expression analysis of the 2765 DE genes using WGCNA found 12 gene modules and eigengenes were calculated for each subject and each module (FIG. 10). Four of these module eigengenes were used in the classification analysis together with subject's age as predictor. Logistic regression of diagnosis with age as predictor produced 1.07 odds ratio (P<0.05) and classification without age was 3-4% less accurate (data not shown). Of the 405 dysregulated genes in both Discovery and Replication subjects, 24.2% ( 98/405; Hyp. P=2.7e-48) were represented in these four modules. Logistic regression with repeated (3×) 10-fold cross-validation and ROC analysis displayed high AUC in both Discovery (training set) and Replication (independent validation test set) toddlers with 82.5% and 75% accuracy, respectively (FIG. 4C, D; Table 14). While specificity remained high across the different class comparisons, accuracy and sensitivity decreased as the samples size was reduced (FIG. 4D).

Characteristics of Genes in the Classification Signature

Metacore analysis of the four modules classifier displayed significant enrichment in translation and immune/inflammation genes (Table 14). DAPPLE analysis (broadinstitute.org/mpg/dapple)²⁰ of these gene modules revealed a statistical enrichment for protein-protein interaction (P<0.001). We next created a classification network based on the genes with the highest number of interactions. Consistent with the enrichment findings, a substantial number of ribosomal and translation genes were positioned at the center of the network (FIG. 4E). Enrichment analysis of the DAPPLE priority genes confirmed translation initiation as top process network (P=4e-18). Moreover, 17.2% of the classifier genes (131/762; Hyp. P=0.046) were located within Autism relevant CNVs (mindspec.org/autdb.html) of size below 1 Mb. This is in line with previous findings²¹ suggesting CNVs as one potential genetic mechanism of gene expression dysregulation²².

Comparison with recently reported classifiers^(23,24) displayed modest to low overlap in gene content. Twelve ( 12/55) and eighteen ( 18/43) reported genes were differentially expressed in the Discovery subjects with only two and one genes, respectively, were present in our classifier (Table 15).

Prediction Performance and Subject Characteristics

Prediction performance of all classified subjects (n=215) was correlated with age, diagnostic sub-groups, clinical and brain measures. Misclassified ASD toddlers were significantly younger, and misclassified control toddlers were significantly older than their correctly classified peers (FIG. 11); no other measure was found to be significantly different (FIG. 12).

A majority of the subjects were Caucasian, Hispanic or Mixed (58.4%, 22.4%, and 12.6% respectively). Of these groups, Mixed and Hispanic subjects were more accurately classified (97% and 88%), compared to Caucasians (74%). At a 0.5 threshold, 12 of the 14 miss-classified ASD subjects were genotyped for CNV analyses. A rare CNV of known ASD etiology, CNTNAP2 duplication, was found in only one subject (Table 7).

TABLE 7 CNV analysis of mis-classified ASD subjects CNV location DEL/ SubjectID (hg18) Size (bp) DUP Genes involved X3F5T chr6: 169182781- 540,264 DUP AK055570, 169723045 BX648586, THBS2, WDR27 X3F5T chr7: 147713357- 106,109 DUP CNTNAP2, 147819466 LOC392145 M8K5X chr20: 47589174- 22,207 DEL PTGIS 47611381 Y2B4P chr15: 21744675- 34,003 DEL Intergenic 21778678 (NDN, AK124131) J3L5W chr1: 231796069- 17,644 DEL Intergenic 231813713 (KIAA1804, KCNK1) L5S3Z chr1: 242582713- 18,934 DEL C1orf100 242601647 X2H3X chr12: 72374075- 17,705 DUP Intergenic 72391780 (TRHDE, BC061638) J3L5W chr14: 19754117- 71,010 DEL OR11H4, OR11H6 19825127 S3D7F chr5: 12578748- 327,822 DEL AY328033, 12906570 AY330599 Z3W7W chr6: 132884089- 22,152 DEL TAAR9 132906241 DEL = heterozygous deletion, DUP = duplication. Reference genome hg18

TABLE 8 Process Networks (Metacore) enrichment for the seven module with significant association with neuroanatomic measures by WGCNA across ASD and control toddlers # Networks pValue Ratio GreenYellow_genelist 1 Cell cycle_Core 9.34E−40 40/115 2 Cell cycle_Mitosis 2.27E−36 44/179 3 Cytoskeleton_Spindle 2.05E−30 33/109 microtubules 4 Cell cycle_S phase 2.59E−29 36/149 5 Cell cycle_G2-M 9.81E−17 29/206 6 Cell cycle_G1-S 1.14E−10 20/163 7 Cytoskeleton_Cytoplasmic 6.53E−07 13/115 microtubules 8 DNA damage_DBS 7.23E−07 13/116 repair 9 DNA 1.56E−06 13/124 damage_Checkpoint 10 Cell cycle_Meiosis 1.77E−06 12/106 11 Proteolysis_Ubiquitin- 1.67E−04 12/166 proteasomal proteolysis Cyan_genelist 1 Cytoskeleton_Regulation 2.87E−04  7/183 of cytoskeleton rearrangement 2 Development_Hemopoiesis, 3.79E−04  6/136 Erythropoietin pathway 3 Cell adhesion_Integrin- 7.38E−04  7/214 mediated cell-matrix adhesion 4 Cytoskeleton_Actin 1.47E−03  6/176 filaments Turquoise_genelist 1 Inflammation_Interferon 1.70E−06 24/110 signaling 2 Inflammation_TREM1 3.14E−06 28/145 signaling 3 Inflammation_NK cell 4.48E−06 30/164 cytotoxicity 4 Development_Blood 6.73E−06 37/228 vessel morphogenesis 5 Protein folding_Folding 7.32E−06 24/119 in normal condition 6 Immune response_TCR 1.52E−05 30/174 signaling 7 Inflammation_Amphoterin 6.26E−05 22/118 signaling 8 Chemotaxis 8.33E−05 24/137 9 Proliferation_Negative 1.15E−04 29/184 regulation of cell proliferation 10 Protein 1.58E−04 15/69  folding_Response to unfolded proteins 11 Apoptosis_Death 3.30E−04 21/123 Domain receptors & caspases in apoptosis MidnightBlue_genelist 1 Cell adhesion_Integrin- 5.41E−10 18/214 mediated cell-matrix adhesion 2 Cell adhesion_Platelet- 1.24E−08 15/174 endothelium-leucocyte interactions 3 Cell adhesion_Platelet 2.20E−07 13/158 aggregation 4 Muscle contraction 4.10E−06 12/173 5 Blood coagulation 4.26E−05 8/94 6 Cytoskeleton_Actin 7.00E−04  9/176 filaments 7 Cytoskeleton_Regulation 9.26E−04  9/183 of cytoskeleton rearrangement 8 Inflammation_Histamine 2.57E−03  9/212 signaling 9 Proliferation_Positive 3.40E−03  9/221 regulation cell proliferation 10 Development_Skeletal 3.70E−03  7/144 muscle development Grey60_genelist 1 Protein folding_ER and 2.34E−08 6/45 cytoplasm 2 Protein folding_Response 3.21E−07 6/69 to unfolded proteins 3 Apoptosis_Endoplasmic 1.28E−06 6/87 reticulum stress pathway 4 Protein folding_Folding 1.20E−04  5/119 in normal condition 5 Immune 4.34E−04  6/243 response_Phagosome in antigen presentation 6 Immune 1.23E−03  5/197 response_Antigen presentation 7 Muscle contraction_Nitric 1.72E−03  4/125 oxide signaling in the cardiovascular system 8 Protein folding_Protein 1.76E−03 3/58 folding nucleus Yellow_genelist 1 Translation_Regulation of 3.13E−08 19/127 initiation 2 Translation_Translation 8.83E−07 21/187 in mitochondria 3 Signal 9.71E−06 14/106 Transduction_Cholecystokinin signaling 4 Inflammation_Neutrophil 1.17E−04 19/219 activation 5 Immune 1.40E−04 19/222 response_Phagocytosis 6 Development_Hemopoiesis, 1.61E−04 14/136 Erythropoietin pathway 7 Cell adhesion_Integrin 2.75E−04 12/110 priming 8 Development_EMT_Regulation 5.07E−04 18/226 of epithelial-to- mesenchymal transition 9 Reproduction_Spermatogenesis, 5.94E−04 18/229 motility and copulation 10 Signal transduction_WNT 7.85E−04 15/177 signaling 11 Apoptosis_Anti- 8.82E−04 15/179 Apoptosis mediated by external signals via MAPK and JAK/STAT LightGreen_genelist 1 Inflammation_NK cell 4.90E−16 18/164 cytotoxicity 2 Immune 3.35E−05  9/197 response_Antigen presentation 3 Inflammation_Jak-STAT 1.57E−04  8/188 Pathway 4 Chemotaxis 9.52E−04  6/137 5 Cell adhesion_Leucocyte 1.53E−03  7/205 chemotaxis

TABLE 9 Process networks (Metacore) enrichment for each of the 2 modules associated with neuroanatomic measures from the WGCNA analysis using control toddlers # Networks pValue Ratio Magenta_genelist 1 Cell cycle_Core 1.73E−39 37/115 2 Cell cycle_Mitosis 9.92E−36 40/179 3 Cell cycle_S phase 1.62E−30 34/149 4 Cytoskeleton_Spindle microtubules 1.49E−29 30/109 5 Cell cycle_G2-M 1.05E−19 29/206 6 Cell cycle_G1-S 7.99E−09 16/163 7 DNA damage_Checkpoint 1.02E−07 13/124 8 Cell cycle_Meiosis 8.62E−06 10/106 9 DNA damage_MMR repair 4.69E−05 7/59 10 Cytoskeleton_Cytoplasmic microtubules 1.09E−04  9/115 MidnightBlue_genelist 1 Protein folding_ER and cytoplasm 6.17E−06 4/45 2 Protein folding_Response to unfolded proteins 3.43E−05 4/69 3 Immune response_Phagosome in antigen 4.45E−04  5/243 presentation 4 Proteolysis_Ubiquitin-proteasomal proteolysis 1.02E−03  4/166 5 Apoptosis_Endoplasmic reticulum stress 1.71E−03 3/87 pathway 6 Immune response_Antigen presentation 1.92E−03  4/197 7 Protein folding_Folding in normal condition 4.17E−03  3/119 8 Signal transduction_Androgen receptor 4.90E−03  3/126 nuclear signaling

TABLE 10 Process networks (Metacore) enrichment for each of the 11 modules associated with neuroanatomic measures from the WGCNA analysis using ASD toddlers # Networks pValue Ratio Yellow_genelist 1 Inflammation_NK cell 7.67E−08 23/164 cytotoxicity 2 Cell adhesion_Leucocyte 1.22E−06 24/205 chemotaxis 3 Chemotaxis 1.29E−06 19/137 4 Inflammation_TREM1 1.21E−05 18/145 signaling 5 Immune response_TCR 1.28E−05 20/174 signaling 6 Immune response_BCR 2.14E−05 17/137 pathway 7 Inflammation_Innate 2.31E−05 20/181 inflammatory response 8 Immune response_T 2.85E−05 17/140 helper cell differentiation 9 Development_Blood 7.41E−05 22/228 vessel morphogenesis 10 Signal 1.39E−04 11/75  transduction_ERBB- family signaling Salmon_genelist 1 Cell adhesion_Integrin- 7.07E−08 16/214 mediated cell-matrix adhesion 2 Muscle contraction 1.89E−07 14/173 3 Cell adhesion_Platelet- 2.03E−07 14/174 endothelium-leucocyte interactions 4 Cell adhesion_Platelet 2.97E−06 12/158 aggregation 5 Blood coagulation 6.50E−05 8/94 6 Cytoskeleton_Actin 1.07E−03  9/176 filaments 7 Proliferation_Positive 1.44E−03 10/221 regulation cell proliferation Royalblue_genelist 1 Protein 3.21E−07 6/69 folding_Response to unfolded proteins 2 Apoptosis_Endoplasmic 2.67E−05 5/87 reticulum stress pathway 3 Protein folding_ER and 3.34E−05 4/45 cytoplasm 4 Immune 5.02E−05  7/243 response_Phagosome in antigen presentation 5 Immune 1.23E−03  5/197 response_Antigen presentation 6 Muscle 1.72E−03  4/125 contraction_Nitric oxide signaling in the cardiovascular system Brown_genelist 1 Development_Blood 2.08E−06 26/228 vessel morphogenesis 2 Chemotaxis 1.52E−04 16/137 3 Cell adhesion_Leucocyte 2.87E−04 20/205 chemotaxis 4 Immune response_IL-5 3.71E−04 8/44 signalling 5 Apoptosis_Death 5.10E−04 14/123 Domain receptors & caspases in apoptosis 6 Proliferation_Negative 5.57E−04 18/184 regulation of cell proliferation 7 Inflammation_Neutrophil 6.78E−04 20/219 activation 8 Reproduction_Feeding 1.09E−03 19/211 and Neurohormone signaling 9 Reproduction_Progesterone 1.22E−03 19/213 signaling 10 Development_Hedgehog 1.80E−03 21/254 signaling Purple_genelist 1 Cell cycle_Core 1.03E−43 42/115 2 Cell cycle_Mitosis 1.08E−30 39/179 3 Cell cycle_S phase 5.25E−30 36/149 4 Cytoskeleton_Spindle 3.84E−24 28/109 microtubules 5 Cell cycle_G2-M 3.03E−17 29/206 6 Cell cycle_G1-S 5.30E−11 20/163 7 DNA 6.06E−06 12/124 damage_Checkpoint 8 DNA damage_DBS 1.83E−05 11/116 repair 9 DNA 1.59E−04 7/59 damage_MMR repair 10 DNA damage_BER- 3.34E−04  9/110 NER repair Grey60_genelist 1 Translation_Translation 1.35E−09 11/171 initiation 2 Translation_Elongation- 2.66E−04  7/233 Termination Green_genelist 1 Inflammation_Interferon 1.18E−31 35/110 signaling 2 Immune 4.28E−11 16/84  response_Innate immune response to RNA viral infection 3 Inflammation_Inflammasome 2.45E−06 13/118 4 Immune 1.50E−04 14/197 response_Antigen presentation 5 Inflammation_IFN- 1.91E−04 10/110 gamma signaling 6 Inflammation_Complement 2.35E−04 8/73 system 7 Chemotaxis 2.75E−04 11/137 Black_genelist 1 Immune 2.32E−05 11/174 response_TCR signaling 2 Translation_Regulation 3.29E−04  8/127 of initiation

TABLE 11 Process Networks (Metacore) enrichment of the Discovery DE genes # Networks pValue Ratio # Map folders pValue Ratio 1 Apoptosis_Apoptotic 4.00E−07 43/159 1 Immune system 6.72E−24 169/1000 nucleus response 2 Apoptosis_Death Domain 3.87E−06 34/123 2 Inflammatory 2.80E−14 122/775  receptors & caspases in response apoptosis 3 Immune 1.09E−05 54/243 3 DNA-damage 1.66E−13 71/354 response_Phagosome in response antigen presentation 4 Immune 1.58E−05 50/222 4 Cell cycle and its 1.06E−12 89/516 response_Phagocytosis regulation 5 Immune response_TCR 6.83E−05 40/174 5 Apoptosis 3.71E−12 135/953  signaling 6 Translation_Translation 1.01E−04 39/171 6 Cell differentiation 5.52E−10 127/940  initiation 7 Inflammation_Interferon 1.35E−04 28/110 7 Tissue remodeling 1.11E−09 86/557 signaling and wound repair 8 Apoptosis_Anti-apoptosis 1.59E−04 28/111 8 Protein synthesis 2.93E−09 56/306 mediated by external signals via NF-kB 9 Cell adhesion_Leucocyte 1.67E−04 44/205 9 Vascular 1.88E−08 81/543 chemotaxis development (Angiogenesis) 10 Inflammation_IFN- 7.96E−04 26/110 10 Cystic fibrosis 3.70E−08 90/636 gamma signaling disease 11 Transcription_mRNA 1.07E−03 34/160 11 Calcium signaling 1.95E−07 70/469 processing 12 Signal 1.09E−03 33/154 12 Protein degradation 2.55E−07 47/269 Transduction_TGF-beta, GDF and Activin signaling 13 Cell cycle_Mitosis 1.13E−03 37/179 13 Mitogenic signaling 7.87E−07 78/562 14 Cytoskeleton_Actin 1.59E−03 36/176 14 Obesity 1.60E−04 33/211 filaments 15 Cell adhesion_Platelet 1.71E−03 33/158 15 Myogenesis 1.74E−04 19/95  aggregation regulation 16 Reproduction_Progesterone 2.63E−03 41/213 16 Transcription 4.46E−04 15/71  signaling regulation 17 Proteolysis_Proteolysis in 2.64E−03 27/125 17 Hypoxia response 4.64E−04 Nov-43 cell cycle and apoptosis regulation 18 Reproduction_FSH-beta 4.07E−03 32/160 18 Hematopoiesis 2.30E−03 40/313 signaling pathway 19 Cell cycle_G2-M 4.45E−03 39/206 19 Cardiac 4.62E−03 31/236 Hypertrophy 20 Signal 4.99E−03 23/106 20 Blood clotting 9.88E−03 34/279 Transduction_Cholecystokinin signaling 21 Cytoskeleton_Regulation 5.79E−03 35/183 of cytoskeleton rearrangement 22 Development_Regulation 6.11E−03 41/223 of angiogenesis 23 Development_Melanocyte 6.80E−03 13/50  development and pigmentation 24 Inflammation_IL-4 6.98E−03 24/115 signaling 25 Proteolysis_Ubiquitin- 7.21E−03 32/166 proteasomal proteolysis 26 Inflammation_Neutrophil 7.52E−03 40/219 activation

TABLE 12 Pathway comparison between discovery and replication datasets Map −log err(−log # folders (pValue) pValue (pValue)) Ratio 1 DNA-damage 12.6825635 2.08E−13 0.007 115/354  response 12.87386859 1.34E−13 2 Mitogenic 5.963371105 1.09E−06 0.01 131/564  signaling 6.084336396 8.24E−07 3 Hematopoiesis 2.603451962 2.49E−03 0.023 66/313 2.72607322 1.88E−03 4 Cardiac 2.307858391 4.92E−03 0.024 50/236 Hypertrophy 2.422163659 3.78E−03 5 Retinoid 0.201280315 6.29E−01 0.037 13/105 signaling 0.216596719 6.07E−01 6 Androgen 0.439615077 3.63E−01 0.046 32/224 signaling 0.401100113 3.97E−01 7 Lipid 0.300856313 5.00E−01 0.053 51/389 Biosynthesis 0.334325219 4.63E−01 and regulation 8 Transcription 3.33003263 4.68E−04 0.059 25/71  regulation 2.960189446 1.10E−03 9 Neuro- 0.416914634 3.83E−01 0.134 94/720 transmission 0.318216234 4.81E−01 10 Cystic fibrosis 7.338376591 4.59E−08 0.184 142/636  disease 5.052909536 8.85E−06 11 Vascular 8.300162274 5.01E−09 0.234 123/553  development 5.156580335 6.97E−06 (Angiogenesis) 12 Cell cycle and 11.86264589 1.37E−12 0.235 158/516  its regulation 19.1562068 6.98E−20 13 Vasodilation 0.991825816 1.02E−01 0.256 65/402 1.67325462 2.12E−02 14 Apoptosis 11.57348874 2.67E−12 0.27 206/964  6.646276062 2.26E−07 15 Cell 9.182897596 6.56E−10 0.276 197/958  differentiation 5.207328211 6.20E−06 16 Vasocon- 0.424927674 3.76E−01 0.297 51/357 striction 0.783570169 1.65E−01 17 Myogenesis 3.735654493 1.84E−04 0.339 28/95  regulation 1.845576027 1.43E−02 18 Visual 0.187353984 6.50E−01 0.399 15/133 perception 0.080451241 8.31E−01 19 Oxidative 0.351542406 4.45E−01 0.412 93/697 stress 0.844967771 1.43E−01 regulation 20 Calcium 6.633763876 2.32E−07 0.471 101/469  signaling 2.38341947 4.14E−03 21 Tissue 9.020315 9.54E−10 0.489 126/562  remodeling 3.098542 7.97E−04 and wound repair 22 Nicotine 0.160836 6.91E−01 0.507 21/229 action 0.052566 8.86E−01 23 Estrogen 0.32413 4.74E−01 0.508 43/287 signaling 0.993962 1.01E−01 24 Diuresis 0.092696 8.08E−01 0.519 13/139 0.292771 5.10E−01 25 Protein 6.535809 2.91E−07 0.547 65/269 degradation 1.91364 1.22E−02 26 Protein 8.461803 3.45E−09 0.564 79/306 synthesis 2.360215 4.36E−03 27 Obesity 3.377786 4.19E−04 0.566 41/203 0.935168 1.16E−01 28 Inflammatory 13.93779 1.15E−14 0.574 179/790  response 3.775208 1.68E−04 29 Nucleotide 0.039482 9.13E−01 0.601 42/401 metabolism 0.009839 9.78E−01 and its regulation 30 Hypoxia 3.317314 4.82E−04 0.632 13/43  response 0.748119 1.79E−01 regulation 31 Nuclear 0.110138 7.76E−01 0.648 75/595 receptor 0.515415 3.05E−01 signaling 32 Energy 0.311669 4.88E−01 0.682 133/927  metabolism 1.649752 2.24E−02 and its regulation 33 Blood 1.977572 1.05E−02 0.727 48/279 clotting 0.313006 4.86E−01 34 Immune 23.03588 9.21E−24 0.765 233/1007 system 3.06118 8.69E−04 response 35 Spermato- 1.540909 2.88E−02 0.88 6/22 genesis 0.098378 7.97E−01 36 Phospholipid 0.004935 9.89E−01 0.912 17/205 Metabolism 0.108128 7.80E−01 37 Cholesterol 4.34E−05 1.00E+00 0.995 38/471 and bile 0.015158 9.66E−01 acid homeostasis 38 Aminoacid 0 1.00E+00 1 69/944 metabolism 4.34E−05 1.00E+00 and its regulation 39 Vitamin 0 1.00E+00 1 34/688 and 0 1.00E+00 cofactor metabolism and its regulation

TABLE 13 Commonly dysregulated pathways in discovery and replication toddlers # Map folders -log(pValue) pValue Ratio 1 DNA-damage response 6.598599 2.52E−07 20/354 2 Cell cycle and its regulation 5.160019 6.92E−06 22/516 3 Apoptosis 4.645892 2.26E−05 31/964 4 Vascular development 4.177832 6.64E−05 21/553 (Angiogenesis) 5 Obesity 2.446238 3.58E−03  9/203 6 Immune system response 2.430275 3.71E−03  26/1007 7 Cell differentiation 2.406936 3.92E−03 25/958 8 Tissue remodeling and 2.353302 4.43E−03 17/562 wound repair 9 Cardiac Hypertrophy 2.025304 9.43E−03  9/236 10 Mitogenic signaling 1.977159 1.05E−02 16/564

TABLE 14 Process Networks and Pathway Maps (Metacore) enrichment of the four genes modules used as classifier # -log(pValue) pValue Ratio Networks 1 Translation_Translation initiation 9.130416292 7.41E−10 27/171 2 Inflammation_IFN-gamma signaling 5.798876103 1.59E−06 17/110 3 Translation_Elongation-Termination 5.696587929 2.01E−06 26/233 4 Translation_Elongation-Termination_test 5.696587929 2.01E−06 26/233 5 Cell adhesion_Platelet aggregation 5.322575562 4.76E−06 20/158 6 Immune response_Phagocytosis 5.056653902 8.78E−06 24/222 7 Cell adhesion_Leucocyte chemotaxis 4.141102043 7.23E−05 21/205 8 Signal Transduction_Cholecystokinin signaling 4.088735829 8.15E−05 14/106 9 Immune response_TCR signaling 3.677367288 2.10E−04 18/174 10 Cell cycle_G1-S Growth factor regulation 3.513144645 3.07E−04 19/195 Map folders 1 Immune system response 11.64859025 2.25E−12 66/1007 2 Protein synthesis 9.648590248 2.25E−10 31/306 3 Tissue remodeling and wound repair 8.799423073 1.59E−09 42/562 4 Inflammatory response 7.558461961 2.76E−08 49/790 5 Vascular development (Angiogenesis) 7.451610582 3.54E−08 39/553 6 Calcium signaling 7.297741837 5.04E−08 35/469 7 Cell differentiation 6.130416292 7.41E−07 52/958 8 Mitogenic signaling 5.813326133 1.54E−06 36/564 9 Hypoxia response regulation 5.684659523 2.07E−06 9/43 10 Cystic fibrosis disease 5.016779785 9.62E−06 37/636

TABLE 15 Kong et al., signature genes  ADAM10 AHNAK CREBBP overlapping the DE genes IFNAR2KBTBD11 KIAA0247 from the discovery subjects KIDINS220 MGAT4A PTPRE ROCK1 SERINC3 ZNF12 Glatt et al., signature genes ANKRD22 ANXA3 APOBEC3G overlapping the DE genes  C11orf75 C3orf38 CARD17 from the discovery subjects FCGR1A FCGR1B  GBP1 GBP5 GCH1 IFI16  IL1RN LOC644852 PARP9 PLSCR1TAP1 VWF Kong et al., signature genes AHNAK CREBBP KBTBD11 overlapping with the   KIAA0247 KIDINS220 four gene modules classifier ROCK1 Glatt et al., signature genes VWF overlapping with the four gene modules classifier

TABLE 16 Gene Listing of Unique Differentially Expressed (DE) Genes SEPT6 SEPT7 SEPT9 SEPT11 SEPT14 AAK1 ABAT ABCB1 ABCC3 ABCG1 ABHD13 ABHD14A ABHD14B ABHD15 ABHD7 ABL1 ACAA1 ACACB ACAD11 ACAD8 ACADVL ACD ACER2 ACOT4 ACOT9 ACSL1 ACSM3 ACTA2 ACTR2 ACYP2 ADAM10 ADAM17 ADAM19 ADAM28 ADARB1 ADCY7 ADI1 ADNP ADNP2 ADPRHL2 AES AFF1 AGAP8 AGER AGPAT3 AHCTF1 AHCY AHI1 AHNAK AIF1 AIM2 AIP AIRE AK2P2 AK3 AK5 AKAP7 AKR1C3 AKR1D1 AKR7A3 AKT1 AKTIP ALDH5A1 ALG10B ALG13 ALKBH7 ALKBH8 ALOX12 ALOX5 ALPK1 ALPP ALS2CR14 AMOTL2 AMY1A AMY1B AMY2B AMZ2 ANGPT1 ANKRD12 ANKRD22 ANKRD28 ANKRD36 ANKRD41 ANKRD44 ANP32A ANP32C ANXA1 ANXA11 ANXA2 ANXA2P1 ANXA2P3 ANXA3 ANXA4 AP1B1 AP1G1 AP1G2 AP1M2 AP1S1 AP2A1 AP2M1 AP2S1 APBA2 API5 APOA1BP APOBEC3G APOL2 APPL2 AQP12A ARAP2 ARAP3 ARF1 ARF6 ARFGAP3 ARHGAP10 ARHGAP17 ARHGAP21 ARHGAP25 ARHGAP27 ARHGAP30 ARHGAP9 ARHGDIA ARHGEF18 ARHGEF3 ARID1A ARID2 ARID4A ARID4B ARL17B ARL4C ARL5A ARL6IP1 ARMC5 ARRB2 ARRDC2 ASAP1 ASB1 ASCC3 ASMTL ATG10 ATG2A ATG3 ATG4C ATHL1 ATM ATN1 ATP1B1 ATP2B4 ATP5A1 ATP5D ATP5E ATP5O ATP6V0C ATP6V1C1 ATPGD1 ATR ATRX AXIN1 AZIN1 B3GALT6 B3GAT1 BAG4 BATF BATF2 BAZ1B BBX BCAP31 BCAS2 BCKDHA BCL11B BCL2 BCL2A1 BCL2L11 BCL6 BCL9 BCL9L BCOR BCORL1 BCR BEGAIN BEX1 BIN2 BIRC3 BIVM BLNK BMF BMP8B BMPR2 BPGM BRD3 BRD7P2 BRDG1 BRPF3 BRWD1 BRWD2 BST1 BTBD2 BTF3 BTK BUB3 C10orf104 C10orf35 C10orf4 C10orf47 C10orf58 C10orf76 C11orf1 C11orf2 C11orf46 C11orf63 C11orf73 C11orf75 C11orf82 C12orf29 C12orf30 C12orf32 C12orf65 C13orf15 C13orf18 C14orf102 C14orf11 C14orf135 C14orf138 C14orf19 C14orf28 C14orf32 C14orf43 C14orf82 C15orf21 C15orf26 C15orf52 C15orf57 C16orf30 C16orf53 C16orf57 C16orf68 C16orf69 C17orf41 C17orf45 C17orf87 C18orf10 C18orf32 C19orf12 C19orf2 C19orf25 C19orf39 C19orf53 C19orf56 C19orf59 C19orf6 C19orf60 C1D C1GALT1 C1GALT1C1 C1orf110 C1orf151 C1orf166 C1orf186 C1orf43 C1orf63 C1orf71 C1orf77 C1orf85 C1orf86 C1orf9 C1QB C1QBP C20orf100 C20orf108 C20orf11 C20orf196 C20orf199 C20orf29 C20orf30 C20orf4 C20orf55 C20orf94 C21orf33 C21orf66 C21orf7 C22orf29 C22orf32 C22orf34 C2orf15 C2orf21 C2orf69 C2orf89 C3orf10 C3orf17 C3orf34 C3orf38 C3orf58 C3orf63 C4orf16 C4orf32 C4orf34 C4orf43 C5orf20 C5orf4 C5orf41 C5orf53 C6orf150 C6orf160 C6orf170 C6orf204 C6orf211 C6orf225 C6orf48 C6orf62 C7orf11 C7orf28A C7orf41 C7orf70 C8orf33 C9orf109 C9orf127 C9orf130 C9orf30 C9orf5 C9orf72 C9orf80 C9orf85 CA2 CA5B CABC1 CABIN1 CABP5 CACYBP CALM1 CALML4 CAMK1D CAMSAP1L1 CANX CAPS2 CAPZA1 CARD14 CARD16 CARD17 CARS2 CASP1 CASP2 CASP4 CASP5 CASP8 CAST CBFB CBL CBR3 CBS CBWD1 CBWD3 CBX7 CCAR1 CCDC115 CCDC117 CCDC147 CCDC15 CCDC16 CCDC23 CCDC25 CCDC28B CCDC50 CCDC59 CCDC6 CCDC65 CCDC72 CCDC82 CCDC86 CCDC90A CCDC90B CCDC91 CCDC97 CCL2 CCL8 CCNG1 CCNK CCNL1 CCNY CCNYL1 CCR4 CCRL2 CCS CCT3 CCT6P1 CD164 CD1E CD27 CD274 CD300LB CD320 CD3D CD3E CD3G CD40LG CD47 CD6 CD74 CD79B CD84 CD97 CD99 CDAN1 CDC14A CDC14B CDC25B CDC2L2 CDC2L6 CDC42SE2 CDK2AP2 CDK5RAP3 CEACAM1 CEACAM4 CECR1 CENPL CENPV CENTB2 CENTD1 CENTG2 CENTG3 CEP27 CEP350 CEP63 CEP68 CEPT1 CERK CETN3 CHCHD2 CHD3 CHD8 CHES1 CHM CHML CHMP2A CHMP5 CHORDC1 CHP CHPF2 CICK0721Q.1 CIR1 CITED4 CKAP5 CKS2 CLASP1 CLEC10A CLEC11A CLEC12A CLEC12B CLEC4A CLEC4D CLEC7A CLIC4 CLIP1 CLIP2 CLIP3 CLK1 CLK3 CLN8 CLSTN1 CMIP CMPK1 CMTM3 CMTM4 CMTM7 CNIH4 CNN3 CNNM3 CNOT1 CNOT7 COBRA1 COL24A1 COMMD7 COMMD8 COPS2 COX11 COX7A2L CPEB3 CPNE1 CR1 CRBN CREBBP CREM CRIP1 CRIP2 CRIPT CROP CRY2 CRYZL1 CS CSDE1 CSE1L CSF2RB CSNK1A1L CSNK1E CTAGE6 CTDP1 CTDSP1 CTDSPL CTNNB1 CTRL CTSB CTSC CTSF CTSL1 CTTN CUGBP2 CUTA CUTC CUTL1 CWC22 CXCL5 CXCR3 CXCR6 CXCR7 CXorf12 CXorf20 CXorf21 CXorf57 CYB561D1 CYB5R1 CYCSL1 CYCSP52 CYFIP2 CYLD CYLN2 CYP20A1 D4S234E DAB2 DACH1 DAP DAP3 DAPK2 DAPP1 DBI DBP DBT DCAF16 DCAF7 DCK DCLRE1C DCTN1 DCTN6 DCXR DDHD1 DDHD2 DDIT4 DDX27 DDX3X DDX3Y DDX41 DDX46 DDX58 DDX59 DDX60 DDX60L DECR2 DEDD DENND2D DERL2 DFFA DGCR8 DGKD DHPS DHRS3 DHRS7 DHX34 DHX9 DIAPH1 DIP2B DKFZp434K191 DKFZp686I15217 DKFZp761P0423 DLEU1 DLEU2 DLEU2L DLGAP4 DMWD DMXL1 DMXL2 DNAJB14 DNAJB2 DNAJC25-GNG10 DNAJC30 DNAJC7 DNHD2 DNHL1 DNM3 DNTT DNTTIP2 DOPEY2 DPEP2 DPM3 DPP3 DRD4 DSC2 DSTN DTWD1 DTX3L DULLARD DUSP14 DUSP22 DYNLT3 DYRK2 ECHDC1 ECT2 EDAR EDC3 EEF1A1 EEF1B2 EEF1G EEF2 EEF2K EFCAB2 EIF2AK1 EIF2AK4 EIF2C2 EIF2S3 EIF3D EIF3F EIF3G EIF3H EIF3K EIF3L EIF4B EIF4E EIF5A ELA1 ELF2 ELMO1 ELOVL4 ENDOD1 ENO2 ENO3 EP300 EP400 EPAS1 EPB41 EPB49 EPHA1 EPHA10 EPHA4 EPN2 EPOR EPSTI1 ERGIC1 ERMN ERMP1 ERVWE1 ESF1 ESYT1 ETFB ETNK1 EVI2B EWSR1 EXOC8 FABP5 FABP5L3 FAHD1 FAIM3 FAM101B FAM102A FAM107B FAM108B1 FAM10A4 FAM116A FAM119A FAM120A FAM122A FAM125B FAM126B FAM134A FAM134B FAM13A FAM153B FAM173A FAM195B FAM19A2 FAM26F FAM3A FAM40B FAM62B FAM65B FAM72D FAM73A FAM84B FAM91A2 FANCL FAS FASTK FBLN1 FBLN2 FBP1 FBXL11 FBXL3 FBXO21 FBXO3 FBXO31 FBXO32 FBXO38 FBXO44 FBXO5 FBXO6 FCER1A FCGBP FCGR1A FCGR1B FCGR1C FCGR2B FCGR2C FCGR3A FCRL3 FERMT3 FEZ1 FEZ2 FFAR2 FHL3 FICD FKBP14 FKBP1A FKBP1P1 FKRP FKTN FLJ10088 FLJ10916 FLJ12078 FLJ13611 FLJ20444 FLJ25363 FLJ34047 FLJ37396 FLJ39639 FLJ42627 FLJ45256 FLT3LG FNBP1 FNIP2 FOXJ2 FOXK1 FOXO1 FOXP1 FTHL11 FTHL16 FTHL2 FTHL3 FTHL8 FTO FTSJ1 FUT6 FXYD5 FYN FYTTD1 FZD7 GABARAPL2 GABBR1 GALNT3 GALNT7 GALT GAR1 GATAD2A GATAD2B GATS GBA GBP1 GBP2 GBP3 GBP5 GBP6 GCC2 GCET2 GCH1 GDI1 GDPD1 GDPD5 GEMIN4 GFI1B GIMAP7 GIPC1 GIYD2 GK GKAP1 GLG1 GLRX5 GLTSCR1 GLTSCR2 GMCL1 GMPPB GNAI2 GNG10 GNG5 GNG7 GNL3L GNPDA2 GNPTAB GOLGA3 GOLGA8B GOLPH3L GOLPH4 GOT2 GP1BA GPAM GPBP1L1 GPN1 GPN3 GPR1 GPR128 GPR141 GPR180 GPR65 GPR68 GPR84 GPR97 GPSM3 GPX4 GRAP2 GRASP GRB14 GRN GRPEL2 GRWD1 GSDM1 GSDMB GSTM1 GSTM2 GSTM3 GSTM4 GSTTP2 GTF2IRD2B GTF3A GTF3C6 GTPBP8 GUCY1A3 GUSBL1 GVIN1 HABP4 HCCA2 HCCS HCFC1 HCFC1R1 HCLS1 HCST HEATR3 HEBP1 HECTD3 HELZ HEMGN HERC1 HERC2 HERPUD2 HEXDC HGD HHEX HIAT1 HIBCH HIGD2A HINT3 HIP1R HIPK2 HIST1H2AD HIST1H2AE HIST2H2AB HK1 HLA-C HLA-DRB4 HLA-DRB6 HLA-H HM13 HMBOX1 HMGB1 HMGN3 HN1 HNRNPA1L2 HNRNPK HNRNPU HNRPC HNRPH1 HNRPH3 HNRPK HNRPUL1 HOMER2 HOOK1 HOOK3 HORMAD1 HOXC4 HOXC6 HPCAL4 HPSE HRSP12 HSCB HSH2D HSP90AB4P HSPA13 HSPA1L HSPA9 HSPB1 HSPCAL3 HVCN1 HYAL3 HYALP1 HYOU1 ICA1 ICK IDH2 IDI1 IFFO2 IFI16 IFI27 IFI44 IFI44L IFI6 IFIT3 IFITM4P IFNAR2 IFT20 IGF2BP2 IGF2BP3 IGF2R IGFL3 IKZF1 IL10 IL10RB IL18RAP IL19 IL1RN IL23A IL25 IL27 IL27RA IL4I1 IL6ST IL7R ILF3 ILK ILVBL IMMP1L IMPA1 IMPA2 INADL ING2 ING3 INPP4B INSM1 INTS1 INTU IP6K1 IP6K2 IPO13 IQCB1 IQGAP2 IRAK1 IRF2 IRF5 IRF7 IRF9 IRS2 IRX3 ISCA1 ISG15 ISG20L2 ITFG1 ITGAL ITGAX ITGB1BP1 ITGB5 ITM2B ITPKB JAM3 JARID1A JARID2 JUP KATNAL1 KBTBD11 KCNA3 KCNG1 KCNH7 KCTD12 KCTD7 KDM1B KDM5B KDM6B KHDRBS1 KHNYN KIAA0040 KIAA0182 KIAA0247 KIAA0319L KIAA0355 KIAA0408 KIAA0776 KIAA1026 KIAA1033 KIAA1147 KIAA1279 KIAA1324 KIAA1430 KIAA1545 KIAA1704 KIAA1715 KIAA1737 KIAA1881 KIAA2026 KIDINS220 KIF13B KIF21B KIF22 KIF2A KIT KLF12 KLF5 KLF6 KLF9 KLHL20 KLHL24 KLHL28 KLRB1 KLRG1 KPNA2 KPNA6 KRCC1 KREMEN1 KREMEN2 KRT40 KRT73 KRT8P9 KRTAP19-6 KTELC1 LACTB LAPTM4A LAPTM4B LAPTM5 LARGE LARP1 LARP1B LASP1 LASS6 LAX1 LCK LCLAT1 LCMT2 LDHA LDHB LDLRAP1 LDOC1L LEF1 LEP LEPROT LFNG LGALS3 LGALS3BP LGALS8 LGALS9 LGALS9B LGMN LGSN LHFPL2 LIAS LIG4 LILRA1 LILRA3 LILRA6 LILRB1 LIMA1 LIMK2 LIMS1 LIN7C LLPH LMF2 LMNB1 LMNB2 LMTK3 LOC100124692 LOC100127893 LOC100127894 LOC100127922 LOC100127975 LOC100127993 LOC100128060 LOC100128062 LOC100128252 LOC100128269 LOC100128274 LOC100128291 LOC100128410 LOC100128460 LOC100128485 LOC100128498 LOC100128516 LOC100128525 LOC100128533 LOC100128548 LOC100128627 LOC100128729 LOC100128731 LOC100128908 LOC100128994 LOC100129055 LOC100129067 LOC100129094 LOC100129139 LOC100129201 LOC100129243 LOC100129267 LOC100129424 LOC100129426 LOC100129441 LOC100129445 LOC100129466 LOC100129502 LOC100129543 LOC100129608 LOC100129637 LOC100129645 LOC100129681 LOC100129686 LOC100129934 LOC100129952 LOC100129960 LOC100129982 LOC100130000 LOC100130053 LOC100130070 LOC100130154 LOC100130171 LOC100130255 LOC100130276 LOC100130289 LOC100130332 LOC100130520 LOC100130550 LOC100130561 LOC100130562 LOC100130598 LOC100130624 LOC100130707 LOC100130715 LOC100130764 LOC100130769 LOC100130892 LOC100130932 LOC100130980 LOC100131076 LOC100131096 LOC100131253 LOC100131349 LOC100131452 LOC100131526 LOC100131572 LOC100131662 LOC100131672 LOC100131675 LOC100131713 LOC100131718 LOC100131810 LOC100131835 LOC100131850 LOC100131866 LOC100131989 LOC100132037 LOC100132086 LOC100132199 LOC100132288 LOC100132323 LOC100132395 LOC100132425 LOC100132444 LOC100132493 LOC100132499 LOC100132510 LOC100132521 LOC100132526 LOC100132547 LOC100132652 LOC100132707 LOC100132717 LOC100132724 LOC100132728 LOC100132742 LOC100132761 LOC100132797 LOC100132804 LOC100132888 LOC100132901 LOC100132920 LOC100133034 LOC100133077 LOC100133080 LOC100133129 LOC100133163 LOC100133177 LOC100133220 LOC100133298 LOC100133329 LOC100133398 LOC100133692 LOC100133697 LOC100133760 LOC100133770 LOC100133803 LOC100133875 LOC100134053 LOC100134159 LOC100134172 LOC100134241 LOC100134291 LOC100134537 LOC100134624 LOC100134688 LOC100134868 LOC100170939 LOC123688 LOC127295 LOC130773 LOC146053 LOC147727 LOC147804 LOC163233 LOC196752 LOC197135 LOC202134 LOC202227 LOC253039 LOC255809 LOC25845 LOC283267 LOC283412 LOC283874 LOC283953 LOC284672 LOC286016 LOC286444 LOC338799 LOC339192 LOC339352 LOC339799 LOC339843 LOC345041 LOC345645 LOC347292 LOC374443 LOC387791 LOC387820 LOC387841 LOC387934 LOC388122 LOC388339 LOC388556 LOC388564 LOC388955 LOC389053 LOC389168 LOC389286 LOC389322 LOC389342 LOC389386 LOC389404 LOC389765 LOC389816 LOC390183 LOC390345 LOC390414 LOC390530 LOC390578 LOC390735 LOC390876 LOC391045 LOC391169 LOC391334 LOC391655 LOC391670 LOC391769 LOC391825 LOC391833 LOC392288 LOC392501 LOC399881 LOC399988 LOC400061 LOC400389 LOC400446 LOC400455 LOC400464 LOC400652 LOC400750 LOC400759 LOC400836 LOC400948 LOC400963 LOC401076 LOC401252 LOC401537 LOC401623 LOC401717 LOC401817 LOC401845 LOC402057 LOC402112 LOC402221 LOC402562 LOC402677 LOC402694 LOC439949 LOC439992 LOC440055 LOC440093 LOC440157 LOC440280 LOC440396 LOC440525 LOC440563 LOC440595 LOC440737 LOC440776 LOC440926 LOC440927 LOC441013 LOC441032 LOC441154 LOC441155 LOC441246 LOC441642 LOC441907 LOC441956 LOC442064 LOC442153 LOC442181 LOC442232 LOC442270 LOC442319 LOC442517 LOC442582 LOC552889 LOC641727 LOC641746 LOC641848 LOC641849 LOC641989 LOC641992 LOC642017 LOC642031 LOC642033 LOC642035 LOC642073 LOC642076 LOC642083 LOC642118 LOC642120 LOC642178 LOC642222 LOC642236 LOC642250 LOC642299 LOC642357 LOC642393 LOC642443 LOC642458 LOC642502 LOC642567 LOC642585 LOC642738 LOC642741 LOC642755 LOC642909 LOC642954 LOC642974 LOC643007 LOC643015 LOC643031 LOC643187 LOC643272 LOC643384 LOC643387 LOC643424 LOC643433 LOC643531 LOC643534 LOC643550 LOC643668 LOC643680 LOC643779 LOC643870 LOC643882 LOC643896 LOC643960 LOC643980 LOC643997 LOC644029 LOC644037 LOC644063 LOC644094 LOC644101 LOC644131 LOC644315 LOC644330 LOC644380 LOC644464 LOC644482 LOC644496 LOC644577 LOC644642 LOC644655 LOC644745 LOC644774 LOC644799 LOC644852 LOC644877 LOC644931 LOC644964 LOC645018 LOC645052 LOC645086 LOC645173 LOC645233 LOC645236 LOC645251 LOC645351 LOC645452 LOC645489 LOC645515 LOC645630 LOC645691 LOC645693 LOC645715 LOC645737 LOC645762 LOC645944 LOC645968 LOC646034 LOC646044 LOC646197 LOC646294 LOC646491 LOC646527 LOC646531 LOC646630 LOC646672 LOC646688 LOC646766 LOC646784 LOC646785 LOC646808 LOC646821 LOC646836 LOC646841 LOC646897 LOC646900 LOC646909 LOC646942 LOC646949 LOC646956 LOC646966 LOC646996 LOC647030 LOC647037 LOC647074 LOC647086 LOC647195 LOC647276 LOC647460 LOC647654 LOC647908 LOC648059 LOC648283 LOC648343 LOC648509 LOC648526 LOC648638 LOC648705 LOC648733 LOC648740 LOC648749 LOC648822 LOC648863 LOC648907 LOC648921 LOC648980 LOC648984 LOC649088 LOC649150 LOC649209 LOC649214 LOC649260 LOC649330 LOC649447 LOC649456 LOC649801 LOC649821 LOC649839 LOC649873 LOC650321 LOC650638 LOC650737 LOC650898 LOC651064 LOC651198 LOC651316 LOC651738 LOC651816 LOC651919 LOC652113 LOC652274 LOC652750 LOC652755 LOC652837 LOC653056 LOC653080 LOC653086 LOC653105 LOC653115 LOC653157 LOC653162 LOC653316 LOC653324 LOC653375 LOC653450 LOC653486 LOC653489 LOC653496 LOC653559 LOC653596 LOC653737 LOC653829 LOC653884 LOC653888 LOC653994 LOC654074 LOC654096 LOC654121 LOC654346 LOC654350 LOC727762 LOC727821 LOC727848 LOC727962 LOC727970 LOC728002 LOC728026 LOC728031 LOC728060 LOC728093 LOC728105 LOC728115 LOC728128 LOC728170 LOC728179 LOC728207 LOC728310 LOC728416 LOC728428 LOC728457 LOC728499 LOC728519 LOC728576 LOC728602 LOC728608 LOC728650 LOC728661 LOC728666 LOC728715 LOC728744 LOC728748 LOC728755 LOC728820 LOC728908 LOC728953 LOC728973 LOC729143 LOC729196 LOC729200 LOC729236 LOC729255 LOC729279 LOC729342 LOC729366 LOC729369 LOC729397 LOC729402 LOC729409 LOC729423 LOC729505 LOC729510 LOC729513 LOC729519 LOC729645 LOC729652 LOC729677 LOC729679 LOC729683 LOC729686 LOC729687 LOC729692 LOC729739 LOC729760 LOC729764 LOC729779 LOC729789 LOC729798 LOC729806 LOC729843 LOC729898 LOC729985 LOC730029 LOC730052 LOC730060 LOC730187 LOC730202 LOC730246 LOC730281 LOC730316 LOC730324 LOC730382 LOC730432 LOC730534 LOC730746 LOC730924 LOC730990 LOC730993 LOC731096 LOC731308 LOC731314 LOC731365 LOC731751 LOC731789 LOC732229 LOC732360 LOC92017 LOC92249 LOC92755 LPAR5 LPHN1 LPIN2 LRBA LRFN3 LRIG1 LRPAP1 LRRC14 LRRC16A LRRC26 LRRC40 LRRK2 LSM5 LSP1 LTB4R LUZP1 LYAR LYPLA1 LYRM4 LYRM7 LYSMD3 MAD2L1 MAD2L1BP MAEA MAF MAFF MAGED4B MAGEE1 Magmas MAL MAML3 MAN1C1 MAP1LC3A MAP2K4 MAP3K7IP1 MAP3K8 MAPBPIP MAPK8IP3 MAPKAPK2 MAPRE3 MARCKSL1 MARS2 MAST3 MAZ MBD2 MBD3 MBOAT2 MBP MBTPS1 MCART1 MCHR2 MCM3APAS MCTP1 MCTP2 MCTS1 MDC1 MDH2 ME2 MED21 MED24 MED31 MEF2A MEF2C MEF2D MEGF6 MEIS1 METAP1 METTL9 MFNG MGAT3 MGAT4A MGC10997 MGC12760 MGC13005 MGC21881 MGC26356 MGC3020 MGC40489 MGC42367 MGC4677 MGC52498 MGC87895 MID1IP1 MIER2 MIIP MIR1299 MIR142 MIR1974 MIR2116 MIR574 MIR877 MIR98 MIS12 MLEC MLKL MLL5 MMGT1 MMP28 MNT MOAP1 MOBKL2C MORC2 MPDU1 MPHOSPH10 MPL MPP6 MRI1 MRP63 MRPL17 MRPL3 MRPL40 MRPL41 MRPL43 MRPL44 MRPL45 MRPL47 MRPL55 MRPS10 MRPS15 MRPS25 MRPS27 MRPS34 MRRF MS4A1 MS4A14 MS4A2 MS4A3 MS4A4A MS4A7 MSH2 MSL3 MSRB3 MSX2P1 MTCP1 MTF2 MTHFD2 MTMR14 MTMR3 MTUS1 MTX1 MTX3 MUM1 MUT MVP MXI1 MYB MYCBP2 MYH9 MYO9B MYOM1 MYST3 N4BP2 N4BP2L1 NAALADL1 NACAP1 NAGLU NAGPA NAIP NAP1L1 NAT6 NAT8B NBEA NBL1 NBN NBPF14 NCALD NCBP2 NCF1B NCF4 NCKAP1L NCOA5 NCOR2 NCR3 NCRNA00081 NCRNA00085 NCRNA00092 NCRNA00152 NDE1 NDFIP1 NDRG3 NDUFA4 NDUFA5 NDUFAF3 NDUFB11 NDUFC1 NDUFS1 NEIL2 NELL2 NENF NFATC2IP NFIC NFIX NFKBIA NFKBIB NFKBIL2 NFX1 NFYB NHLRC4 NHP2 NIP7 NIPSNAP3A NLRC5 NLRP1 NLRP12 NLRP7 NLRP8 NLRX1 NME2 NMI NMT2 NNT NOG NOL9 NOTCH2NL NOX4 NPAL3 NPAT NR1D2 NR3C1 NR3C2 NR4A2 NT5C NT5C3L NT5DC1 NT5DC3 NTNG2 NUAK2 NUBPL NUCB2 NUCKS1 NUDCD2 NUDT16L1 NUDT2 NUDT21 NUFIP2 NUMA1 OAF OAS1 OAS3 OASL ODC1 OGFOD1 OLA1 OMA1 OPN1SW OR1J1 OR2A42 OR7E156P ORC5L OSBPL1A OSBPL8 OSTCL OTOF OTUD1 P2RX5 P2RY8 P4HB P704P P76 PA2G4 PABPC1 PABPC4 PACS1 PAFAH2 PAK1IP1 PAK2 PALLD PAN3 PAPD5 PAPSS1 PAPSS2 PAQR4 PAQR8 PARM1 PARP10 PARP14 PARP15 PARP8 PARP9 PATE2 PATL2 PCBP2 PCDHB9 PCDHGB6 PCYOX1 PDCD10 PDCD2 PDCL PDE12 PDE5A PDE7A PDF PDIA3P PDK1 PDPK1 PDZD4 PEBP1 PECI PELI2 PELP1 PEMT PEX11B PEX14 PFKFB3 PFKL PFN2 PFTK1 PGAM1 PGAM4 PGGT1B PGLS PGM2 PGM2L1 PHACTR2 PHAX PHB PHC2 PHC3 PHF11 PHF14 PHF2 PHF20L1 PHKB PHLDB3 PI4K2B PIAS2 PID1 PIGT PIGX PIK3AP1 PIK3CD PIK3CG PIK3IP1 PIK3R1 PIM2 PIM3 PIN1 PION PIP3-E PIP4K2A PIP5K1C PIP5K2A PITPNC1 PJA2 PKIA PKM2 PKN1 PKN2 PLA2G2D PLAA PLAG1 PLAGL1 PLAUR PLCB2 PLCG1 PLCXD1 PLD3 PLD6 PLEKHA1 PLEKHA5 PLEKHB1 PLEKHB2 PLEKHF1 PLIN2 PLSCR1 PLXNA4 PML PMM2 PMS2L1 PMS2L2 PMS2L5 PNKP PNPLA2 PNPLA6 PNPT1 PNRC2 POGK POLDIP3 POLG2 POLK POLR1D POLR1E POLR2A POLR2E POLR2G POLR2J4 POLR2L POLR3GL POM121C POTE2 POTEE PPAPDC2 PPARBP PPFIA1 PPHLN1 PPIB PPID PPIG PPM1B PPM1K PPP1CB PPP1R13B PPP1R15B PPP1R2 PPP2R1A PPP2R2B PPP2R3A PPP2R5C PPP2R5D PPP2R5E PPP4R2 PPPDE2 PPTC7 PRAGMIN PRDM4 PRIM2 PRKAG1 PRKCA PRKCB PRKCB1 PRKCH PRKCQ PRKY PRMT2 PRPF39 PRPF8 PRR13 PRR7 PRRG4 PRRT3 PRUNE PSG3 PSG9 PSIP1 PSMA3 PSMA6 PSMB7 PSMB8 PSMB9 PSMC4 PSMC6 PSRC1 PTBP1 PTDSS2 PTGR2 PTGS1 PTK2B PTMS PTOV1 PTP4A2 PTPLAD1 PTPLAD2 PTPLB PTPN1 PTPN2 PTPN7 PTPRC PTPRE PTPRO PUM1 PURA PVALB PYCARD QARS QRICH1 RAB11FIP1 RAB11FIP4 RAB11FIP5 RAB20 RAB22A RAB24 RAB33A RAB33B RAB37 RAB3IP RAB43 RABGEF1 RAD21 RAD23A RAD23B RAD51 RAG1AP1 RALA RALGPS2 RALY RANBP9 RANGRF RAP1B RAPGEF2 RAPGEF6 RASA2 RASD1 RASSF2 RASSF5 RASSF6 RAVER1 RAX2 RAXL1 RBBP4 RBBP5 RBM11 RBM12B RBM17 RBM3 RBM39 RBM4 RBMS1 RC3H2 RCN2 REC8 RFK RFNG RFX1 RFX4 RGL4 RGMA RGPD1 RGS18 RHBDD2 RHBDF2 RHOF RHOQ RHOT1 RIOK1 RIPK3 RLN2 RN5S9 RN7SK RNASE3 RNASEH2B RNASEN RNF10 RNF103 RNF135 RNF144 RNF144A RNF213 RNF26 RNMT RNPEPL1 RNU12 RNU4ATAC RNU5A RNU6-1 RNY3 ROCK1 ROCK2 ROD1 RPAP2 RPAP3 RPL10A RPL17 RPL22 RPL23A RPL23AP13 RPL26L1 RPL29P2 RPL36 RPL37 RPL4 RPL5 RPL6 RPL7L1 RPL8 RPP40 RPRD2 RPS10P3 RPS14 RPS15 RPS18 RPS29 RPS3 RPS4X RPS5 RPS6 RPS6KA1 RPS6KA2 RPS6KA4 RPS6P1 RPS7 RPS8 RPUSD1 RRBP1 RRP1B RSAD1 RSF1 RSL1D1 RTBDN RTKN2 RTP4 RUFY2 RUNX1 RUNX3 RWDD1 RXRA RYBP S100A10 S100A6 SAC3D1 SAMD8 SAMD9L SAMSN1 SAP30L SBF1 SBK1 SCAMP1 SCARNA16 SCARNA21 SCARNA22 SCARNA5 SCN3A SDAD1 SDHAF1 SDHC SDPR SEC13 SEC16A SEC23A SEC24A SEC62 SELL SELM SELPLG SELS SELT SEMA3E SEMA4D SEMA4F SENP6 SEPN1 SEPW1 SERINC1 SERINC3 SERPINA1 SERPINB8 SERPINE2 SERPING1 SERTAD2 SESN1 SET SETD1A SETD1B SETD6 SF1 SF3A1 SF3A2 SF3B14 SF4 SFRS11 SFRS12 SFRS12IP1 SFRS2B SFRS3 SGK SGK3 SGOL2 SH2B2 SH3BGRL3 SH3BP2 SH3GL1 SH3GLB2 SH3KBP1 SH3PXD2A SIAH1 SIDT2 SIGLEC7 SIGLECP3 SIK3 SIL1 SIN3A SKA2 SKAP1 SKP2 SLA2 SLAMF8 SLC15A2 SLC24A3 SLC25A19 SLC25A23 SLC25A28 SLC25A3 SLC2A14 SLC2A6 SLC35C1 SLC35E1 SLC36A4 SLC38A1 SLC39A11 SLC39A8 SLC44A2 SLC45A3 SLC4A5 SLC5A8 SLC6A10P SLC7A1 SLC7A3 SLC7A6 SLC8A3 SLC9A4 SMA5 SMAD3 SMAD5 SMARCA5 SMARCB1 SMARCC1 SMARCC2 SMC5 SMPD1 SMYD2 SMYD3 SNAPC1 SNHG10 SNHG8 SNHG9 SNORA12 SNORA28 SNORD13 SNORD16 SNORD18C SNORD21 SNORD46 SNORD58B SNORD62B SNORD71 SNORD73A SNORD76 SNORD95 SNRPD3 SNRPE SNUPN SNURF SNX14 SNX17 SNX20 SNX7 SOCS3 SOCS4 SORBS3 SP1 SP100 SP2 SP4 SPC24 SPC25 SPCS1 SPCS2 SPG21 SPI1 SPIN1 SPNS3 SPOCK2 SPTAN1 SPTLC1 SREBF1 SRFBP1 SRM SRP19 SRP72 SRPK2 SRRM2 SS18 SSB SSBP3 SSH1 SSNA1 SSR4 ST6GAL1 ST6GALN AC4 ST6GALN AC6 STAR STARD7 STAT1 STAT4 STK40 STRN4 STX10 STX7 SULT1A2 SULT1A3 SUMF2 SUMO1 SUMO1P3 SURF6 SUV420H1 SVIL SYAP1 SYF2 SYNC1 SYNE1 SYTL2 SYTL3 TACC1 TADA1L TAF1C TAF1D TAF4 TAF8 TAF9 TAGAP TAGLN TAL1 TANK TAP1 TARP TATDN2 TBC1D10B TBC1D22A TBC1D7 TBC1D9B TBCA TBL1X TCEA2 TCEA3 TCEAL4 TCEAL8 TCEB1 TCEB2 TCERG1 TCFL5 TCL1A TCL1B TCP1 TDG TDRD7 TECR TESK1 TFEC TFIP11 TGFBR2 TGIF1 THEX1 THOC2 THOC4 TIAF1 TIAL1 TIFA TIMELESS TIMM10 TIMM22 TIMP2 TLE2 TLK1 TLN1 TLR10 TLR5 TMC6 TMCC1 TMCC3 TMEM106A TMEM107 TMEM109 TMEM111 TMEM116 TMEM126B TMEM137 TMEM156 TMEM165 TMEM185A TMEM189-UBE2V1 TMEM191A TMEM203 TMEM204 TMEM209 TMEM219 TMEM38B TMEM50B TMEM51 TMF1 TMSB4X TMTC4 TMUB1 TMX4 TNFAIP6 TNFAIP8L1 TNFRSF21 TNFRSF25 TNFRSF9 TNFSF10 TNFSF12 TNFSF13 TNFSF13B TNFSF14 TNFSF15 TNIK TNS1 TOB1 TOMM20 TOMM7 TOP1MT TOP1P1 TOP1P2 TOP2B TOX TOX2 TP53BP2 TP53INP2 TPI1 TPM4 TPP2 TPRKB TRA1P2 TRAPPC4 TRAPPC9 TRAT1 TRIM13 TRIM16L TRIM22 TRIM23 TRIM26 TRIM4 TRIM5 TRIM52 TRIM78P TRIM9 TRIOBP TROVE2 TRPC4AP TRRAP TSC22D1 TSC22D3 TSEN15 TSEN54 TSGA14 TSHZ1 TSPAN14 TSPAN5 TSTD1 TTC3 TTC4 TTN TTRAP TUBA1A TUBA3E TUBB4Q TUFM TULP4 TUT1 TWSG1 TYMP TYSND1 U2AF1 UBA3 UBAP2L UBE1C UBE2D1 UBE2D2 UBE2E3 UBE2H UBE2J1 UBE2L6 UBE2O UBE2V1 UBE2W UBE3B UBE4B UBN2 UBXN7 UCRC UGCGL1 UGP2 UHMK1 UHRF2 UIMC1 UNC84B UNC93B1 UNKL UPF3A UQCRH URG4 USH1G USP10 USP13 USP14 USP18 USP33 USP47 USP48 USP5 USP53 USP6 USP9X UXT VAC14 VAMP2 VAV3 VDAC2 VEGFB VEZT VHL VPS13B VPS13C VPS28 VPS41 VPS52 VSIG1 VWF WAS WASH2P WBP11 WBP2 WDFY3 WDR1 WDR23 WDR48 WDR73 WDR74 WDR75 WDR82 WHAMM WNK1 WRB WRNIP1 WWP1 WWP2 XAB2 XAF1 XRCC4 XRCC6 XRN1 XRN2 XYLT2 YES1 YIF1A YIPF4 YOD1 YPEL3 YTHDC1 YY1 ZBED4 ZBTB16 ZBTB3 ZBTB4 ZBTB42 ZBTB43 ZBTB9 ZC3H4 ZC3H5 ZCCHC10 ZCCHC14 ZDHHC4 ZDHHC9 ZFAND1 ZFHX3 ZFP14 ZFP30 ZFP37 ZFP91 ZFPM1 ZFYVE19 ZFYVE27 ZMYND11 ZNF12 ZNF121 ZNF131 ZNF136 ZNF142 ZNF148 ZNF185 ZNF204 ZNF223 ZNF234 ZNF24 ZNF252 ZNF256 ZNF260 ZNF274 ZNF281 ZNF282 ZNF319 ZNF32 ZNF320 ZNF329 ZNF337 ZNF33A ZNF345 ZNF364 ZNF37A ZNF395 ZNF420 ZNF430 ZNF438 ZNF441 ZNF444 ZNF471 ZNF502 ZNF518B ZNF524 ZNF526 ZNF529 ZNF540 ZNF544 ZNF559 ZNF562 ZNF567 ZNF580 ZNF589 ZNF609 ZNF615 ZNF626 ZNF638 ZNF641 ZNF644 ZNF669 ZNF683 ZNF716 ZNF738 ZNF773 ZNF792 ZNF805 ZNF818 ZNF828 ZNF831 ZNF860 ZNF91 ZNF92 ZNF93 ZRSR2 ZSCAN2 ZYG11B CREB1 CLOCK ZNF398 ATXN7L3B MTRNR2L1 ZBED3 PPM1A ZNF160 RORA FBXO22 TRDV3 CCNG2 DDI2 TTC39C ETS1 ZMAT3 LRRC8B ZNF33B TMEM33 GDF11 TNRC6C RAB27B

TABLE 17 Gene Listing of Commonly Dysregulated Genes in Discovery and Replication Toddlers ABCG1 ACACB AGER AGPAT3 AKR1C3 AKR1D1 AKT1 ALG10B ANKRD22 ANKRD44 ANXA1 ANXA3 AP2A1 ARAP3 ARHGAP10 ARHGAP25 ARHGAP30 ARHGAP9 ARL5A ASCC3 ASMTL ATG2A ATG4C ATP1B1 ATP5A1 AXIN1 BIRC3 BMF BPGM BRDG1 C10orf4 C11orf82 C14orf102 C16orf53 C19orf59 C1GALT1 C1GALT1C1 C1QBP C20orf30 C3orf17 C3orf38 C3orf58 C4orf16 C4orf32 C4orf34 C6orf150 C7orf28A C9orf127 C9orf72 C9orf85 CABC1 CABIN1 CAMK1D CAPZA1 CARD17 CBFB CBX7 CCDC117 CCDC50 CCDC90B CCDC91 CCNY CCNYL1 CCS CCT6P1 CD274 CD300LB CD3E CD84 CD97 CDAN1 CDC2L6 CDK2AP2 CDK5RAP3 CENPV CEPT1 CERK CHES1 CHM CHMP5 CHORDC1 CHPF2 CKS2 CLEC4D CLIC4 CNIH4 COMMD8 COPS2 CPNE1 CRY2 CSNK1E CTDP1 CTDSP1 CTSF CXorf57 CYP20A1 DAPK2 DBI DBP DCK DCTN6 DDIT4 DDX60 DHPS DHRS3 DHX34 DLEU1 DNAJB14 DNHD2 DPEP2 DPM3 DRD4 DTWD1 DUSP22 DYNLT3 ECT2 EEF2K EIF3G ENO2 ENO3 EPN2 EPSTI1 ETNK1 FABP5 FAM134A FAM134B FAM153B FAM91A2 FANCL FBXO5 FEZ1 FHL3 FICD FKTN FLJ39639 FOXJ2 FYN FYTTD1 GALT GATAD2B GATS GBP1 GCH1 GNAI2 GNPDA2 GOLPH3L GPR141 GPR68 GPR84 GRASP GSTM1 GSTM2 GTF3C6 GTPBP8 HCCS HERC2 HHEX HIBCH HINT3 HK1 HNRPK HPCAL4 HRSP12 HSPA9 IFI16 IFI27 IGF2BP3 IL6ST IMPA2 INADL IP6K1 IQCB1 ITFG1 ITGAX ITPKB KCNG1 KDM6B KHNYN KIAA0247 KIAA1279 KIAA1715 KIF2A KLHL20 KPNA2 KPNA6 LACTB LDHA LFNG LGALS3BP LGALS8 LMF2 LMTK3 LOC202134 LOC387934 LOC389816 LOC442582 LOC643272 LOC648733 LOC650898 LOC652837 LOC653105 LOC654121 LOC729843 LPIN2 LRRC26 LYPLA1 MAD2L1 MAD2L1BP MAP1LC3A MAPRE3 MAST3 ME2 METAP1 MGAT3 MGC12760 MGC13005 MGC3020 MGC40489 MID1IP1 MLKL MRPL3 MRPL47 MRPS10 MS4A1 MS4A2 MS4A4A MSH2 MTHFD2 MUT MYH9 MYO9B MYOM1 Magmas N4BP2L1 NAALADL1 NAGLU NAT6 NBN NCBP2 NCOR2 NCR3 NDE1 NDRG3 NFATC2IP NFIC NFKBIB NLRP1 NNT NR3C2 NUCB2 NUDT16L1 OMA1 OTOF PACS1 PAFAH2 PARP9 PCYOX1 PDCD10 PDZD4 PFTK1 PGGT1B PHAX PHC3 PHF14 PHF2 PHKB PI4K2B PIAS2 PIGX PIK3CD PITPNC1 PKIA PLCB2 PLD3 PLEKHF1 PLSCR1 PML PNPLA2 PNRC2 POLR1E PPM1K PPPDE2 PSMA3 PSMA6 PSMC6 PTDSS2 PTMS PTP4A2 PTPLAD1 PTPN2 PTPRE PTPRO RAB37 RAD23B RAD51 RALY RASSF2 RBM3 RFNG RFX4 RGPD1 RHBDD2 RHOT1 RIOK1 RN7SK RPAP3 RPL6 RPP40 RPS6KA2 RPS7 RTP4 SAMD9L SAMSN1 SDHAF1 SELL SELM SEMA4D SERPINB8 SF1 SFRS12IP1 SFRS3 SGOL2 SH3GL1 SIGLEC7 SIGLECP3 SLC35E1 SLC39A8 SLC44A2 SLC45A3 SMARCA5 SMARCC2 SNX14 SOCS4 SORBS3 SP100 SPC25 SPNS3 SPTLC1 SREBF1 SRFBP1 SRP72 SS18 SSB SSBP3 STAT1 STRN4 SUMO1P3 SYTL3 TADA1L TANK TBC1D9B TBCA TBL1X TCEB2 TDG THEX1 THOC2 TIFA TLR10 TMEM126B TMEM165 TMTC4 TNFRSF21 TNFSF12 TNFSF14 TP53INP2 TPRKB TRIM22 TRIM78P TRPC4AP TSC22D1 TSC22D3 TSEN54 TSGA14 TSPAN14 UBA3 UBE4B UGP2 UNKL VAMP2 VEZT VPS13B VPS28 VPS41 WDR73 WNK1 WRB XYLT2 YES1 YPEL3 YY1 ZBTB16 ZBTB4 ZFPM1 ZFYVE27 ZNF24 ZNF345 ZNF395 ZNF430 ZNF518B ZNF526 ZNF567 ZNF589 ZNF626 ZNF92

TABLE 18 Gene Listing of DNA-Damage Genes 14-3-3 ATM Bax Bcl-2 ICAD CBP CDK1 (p34) CREB1 DNA ligase IV FasR(CD95) G-protein alpha-s HSP90 I-kB PHAP1 (pp32) MRE11 PCNA AKT(PKB) PLC-beta PP2A catalytic RPA3 RAD23A Rad51 Rb protein p90Rsk STAT1 SOS PDK(PDPK1) XRCC4 Adenylate cyclase Beta- catenin c-Abl Calmodulin Caspase-7 Caspase-8 Cyclin D Nibrin ERK1/2 ATR Ubiquitin PI3K cat class IA PI3K reg class IA MEK4(MAP2K4) C-IAP2 c-IAP1 HSP27 PKC-alpha PKA-cat (cAMP- dependent)p300 Histone H1 Caspase-2 POLR2A Cyclin A HSP70 SUMO-1 Lamin B MKK7 (MAP2K7) PML NCOA1 (SRC1) SP1 MSH2 TDG GLK(MAP4K3) PLK3 (CNK) FHL2 Ku70 SET WRN PP2C Bim BMF MAP1 RAP-1A Caspase-4 EGR1 CDC25B NURR1 POLD cat (p125) Chk1 Keratin 1 NAIP Beta- arrestin2 14-3-3 theta Artemis BFL1 Centrin-2 Chk2 ERCC-1 ERCC8 FANCL HMG2 Histone H2B La protein Lamin B1 MSH3 MUNC13-4 MutSbeta complex N- myristoyltransferase NFBD1 NUMA1 PIAS2 PNKP POLD reg (p12) PTOP RAD23B RBBP8 (CtIP) RPL22 Rab-27A Sirtuin USP1 VDAC2 XAB2 cPKC (conventional) hnRNP A1 hnRNP C p23 co-chaperone

TABLE 19 Gene Listing of Mitogenic Signaling Genes Bax Bcl-2 ERK5 (MAPK7) C3G CBP CDK1 (p34) CREB1 CRK c-Cbl CDC42 ErbB2 FasR(CD95) Fyn G-protein alpha-i family G-protein alpha-s RASA2 HSP90 I-kB JAK2 LIMK2 Lck NF-AT4(NFATC3) PAK2 PCNA AKT(PKB) PKC-zeta PKR PLC-beta PLC-gamma Pim-1 Pyk2(FAK2) Rb protein p90Rsk STAT1 SOS Tyk2 PDK(PDPK1) VEGF-B Adenylate cyclase Beta-catenin Calmodulin Caspase-7 Cyclin D gp130 ERK1/2 SKP2 Paxillin PKC Ubiquitin PI3K cat class IA PI3K reg class IA RPS6 MEK4(MAP2K4) C-IAP2 c-IAP1 MAPKAPK2 HSP27 PKC-beta PKC-alpha ILK PKA-cat (cAMP-dependent) FOX03A RalA p300 MRLC COX-1 (PTGS1) GMF DCOR Cyclin A2 PKC-theta IRS-2 SH2B MKK7 (MAP2K7) NCK1 N-Ras NCOA1 (SRC1) SP1 IBP DOK2 TPL2(MAP3K8) GLK(MAP4K3) RASA3 Sequestosome 1(p62) ICAM1 Bax Bcl-2 ERK5 (MAPK7) C3G CBP CDK1 (p34) CREB1 CRK c-Cbl CDC42 ErbB2 FasR(CD95) Fyn G-protein alpha-i family G-protein alpha-s RASA2 HSP90 I-kB JAK2 LIMK2 Lck NF-AT4(NFATC3) PAK2 PCNA AKT(PKB) PKC-zeta PKR PLC-beta PLC-gamma Pim-1 Pyk2(FAK2) Rb protein p90Rsk STAT1 SOS Tyk2 PDK(PDPK1) VEGF-B Adenylate cyclase Beta-catenin Calmodulin Caspase-7 Cyclin D gp130 ERK1/2 SKP2 Paxillin PKC Ubiquitin PI3K cat class IA PI3K reg class IA RPS6 MEK4(MAP2K4) C-IAP2 c-IAP1 MAPKAPK2 HSP27 PKC-beta PKC-alphaILK PKA-cat (cAMP-dependent) FOXO3A RalA p300 MRLC COX-1 (PTGS1) GMF DCOR Cyclin A2 PKC-theta IRS-2 SH2B MKK7 (MAP2K7) NCK1 N-Ras NCOA1 (SRC1) SP1 IBP DOK2 TPL2(MAP3K8) GLK(MAP4K3) RASA3 Sequestosome 1(p62) ICAM1 BCR PLAUR (uPAR) RAP-1A PDZ-GEF1 MAGI-1(BAIAP1) Tuberin EGR1 NFKBIA CDC25B SOCS3 MEF2C PLGF ERK1 (MAPK3) Angiopoietin 1 PLC-gamma 1 p90RSK1 LPP3 PI3K reg class IA (p85-alpha) Neutral sphingomyelinase DIA1 14-3-3 zeta/delta Acid sphingomyelinase BFL1 BUB1 CCL2 CERK1 GIPC GLCM MLCP (cat) NCOA3 (pCIP/SRC3)PAQR7 PAQR8 PDGF-D PEDF-R (iPLA2-zeta) PELP1 PI3K cat class IA (p110-delta) PI3K reg class IA (p85) PKA-cat alpha RGL2 RNTRE ROCK1 ROCK2 SPT1 TSAD Tcf(Lef) Tob1 WNK1

TABLE 20 Top 30 Genes with the Highest Gene Connectivity Correlated with Brain Size Variation in ASD Module greenyellow DLGAP5 HMMR CEP55 CDKN3 CCNB2 ASPM KIF11 KIAA0101 OIP5 TOP2A BUB1 NUSAP1 TYMS NCAPG CDC45L CCNA2 MCM10 CHEK1 UBE2C AURKA CDC2 CENPE PTTG3P PRC1 CDCA5 MELK UHRF1 MND1 ZWINT GMNN Module grey60 TXNDC5 TNFRSF17 ABCB9 MGC29506 CD38 FKBP11 SEC11C LOC647450 LOC647506 LOC652493 LOC652694 CRKRS IGJ CAMK1G GGH CAV1 GLDC DNAJB11 ELL2 FAM46C IGLL1 ARMET LOC642113 ITM2C HSP90B1 LOC642131 SLC25A4 LOC651751 LOC390712 SDF2L1 Module midnightblue SH3BGRL2 CTDSPL GP9 PDE5A TUBB1 ITGB5 ESAM SEPT5 TREML1 PTGS1 TSPAN9 CTTN NRGN PTCRA SELP ITGA2B MARCH2 MYLK SDPR ALOX12 PEAR1 ACRBP ABLIM3 F13A1 CMTM5 GNG11 DDEF2 C7orf41 ASAP2 ANKRD9 Module yellow SDCBP LRRK2 RP2 FAM49B MNDA UBE2W LOC100129960 NDUFS3 DDX3X PLXNC1 MCL1 JMJD1C CENTB2 ST8SIA4 SNX13 SNX10 ELOVL5 C12orf35 SPAG9 MRPS12 CYB5R4 LOC729279 LYST POMGNT1 SPOPL PELI1 OGFRL1 SHOC2 CDC42EP3 ACSL4 Module cyan LOC440313 SPRYD3 LOC642469 DPYSL5 GPR175 EPB42 SERPINA13 LOC100131726 MUC6 HBD SLC25A39 AHSP SELENBP1 LOC100132499 RNF213 ROPN1B LOC100131391 LOC100131164 STRADB IFIT1L FBXO7 UBXN6 EPB49 HBQ1 ALAS2 SEMA6B TESC HBE1 GUK1 LOC652140 Module turquoise ITPRIP NUMB REPS2 AQP9 SEPX1 STX3 FCGR2A RNF149 BASP1 NCF4 RBM47 NFIL3 MXD1 PHC2 LIMK2 TLR1 GK BCL6 CSF3R GCA LOC730278 SLC22A4 NDEL1 CEACAM3 RALB PFKFB4 LOC654133 PSG3 MANSC1 CXCR1

TABLE 21 Top 30 Genes with the Highest Gene Connectivity Correlated with Brain Size Variation in Control Module greenyellow NCAPG HMMR DLGAP5 CCNB2 CDC20 TOP2A C12orf48 CDKN3 CDC45L CEP55 NUSAP1 BUB1 KIF11 CHEK1 ASPM TYMS CDC2 NEK2 DEPDC1B PTTG3P PTTG1 KIAA0101 AURKA OIP5 MND1 MELK CCNA2 GMNN CDCA5 CCNE2 Module grey60 TNFRSF17 MGC29506 TXNDC5 LOC647450 LOC652493 ABCB9 LOC652694 LOC642113 IGJ LOC647506 CD38 GLDC SEC11C IGLL1 CAMK1G CRKRS FKBP11 ARMET CAV1 FAM46C GGH IGLL3 ITM2C LOC390712 LOC729768 HSP90B1 PRDX4 ELL2 GMPPB DNAJB11 Module midnightblue ITGB5 PDE5A ITGB3 TSPAN9 GP9 TUBB1 PPBP CTDSPL CTTN SDPR PTGS1 NRGN NCKAP5 SEPT5 PTCRA SH3BGRL2 ACRBP ITGA2B ALOX12 TREML1 C5orf4 ESAM ELOVL7 F13A1 GNG11 PROS1 DDEF2 GP1BA ANKRD9 ASAP2 Module yellow SDCBP LRRK2 ZFYVE16 NDUFS3 CPSF4 FAM49B DCTPP1 DNAJC8 KRTCAP2 TMEM154 WDR54 MEGF9 LOC391811 LOC100129960 CMTM6 PELI1 NDUFS8 NUDT1 PLXNC1 SLC12A6 PAFAH1B3 ADSL SPAG9 NHP2 ITPA NDUFB8 SLC40A1 CPEB2 MRPS12 APAF1 Module cyan LOC642469 GPR175 LOC100131726 LOC440313 SPRYD3 MUC6 AHSP HBD SLC25A39 LOC100132499 STRADB EPB42 LOC389599 DPYSL5 SERPINA13 FBXO7 EPB49 UBXN6 LOC100131164 LOC100131391 RNF213 MIR98 SELENBP1 MRPL40 LOC645944 C1orf77 LOC728453 PMM1 HBE1 LOC100130255 Module turquoise GCA NUMB PFKFB4 REPS2 TLR6 SRGN RNF149 TLR1 ACSL1 CSF3R ITPRIP LIMK2 FCGR2A SEPX1 PHC2 LILRB3 STX3 GK FRAT2 FPR1 NFIL3 PSG9 LIN7A S100A11 TNFRSF1A RALB AQP9 NCF4 FTHL12 LAMP2

TABLE 22 Top 30 Genes with the Highest Gene Significance Correlated with Brain Size Variation in ASD Module greenyellow RNASEH2A C6orf129 EBP STOML2 RRM1 RAPGEF5 STMN1 CENPM CCNF TOP2A PSMB7 KIF20A FAM19A2 PDCD1 BIRC5 LOC441455 CDCA5 PHF19 FEN1 MCM2 CCNB2 MND1 RACGAP1 PTTG3P MTHFD1L FABP5L2 CHST12 UBE2T PLS3 CENPA Module grey60 PDIA4 RPN2 MOXD1 MTDH IGLL3 CRKRS HYOU1 LOC647506 BCL2L11 KLHL14 SDF2L1 IGLL1 ABCB9 EAF2 DENND5B IRF4 ARMET TNFRSF17 ITM2C PDIA5 LOC652694 DNAJB11 SPATS2 LOC647460 SEC11C GLDC POU2AF1 LOC541471 C14orf145 MGC29506 Module midnightblue RNF11 PDGFC MPP1 CDC14B TUBB1 TPM1 ZNF185 P2RY12 MMD SDPR NCKAP5 SPOCD1 FHL1 MARCH2 ARHGAP18 ASAP2 VCL FRMD3 CALD1 GNG11 GUCY1B3 LY6G6F F13A1 LEPR JAM3 MYLK BMP6 ELOVL7 PGRMC1 SPARC Module yellow BLMH DDT SSFA2 PHPT1 TLR8 HDAC1 OSGIN2 FAM159A MAPK14 NDUFB9 LAGE3 DMXL2 PDCD2L SLC2A1 NTHL1 STRA13 NPM3 HIST1H2AC C6orf108 LCP2 CLPP NDUFA7 MRPL55 MCTP1 WBSCR22 MFSD1 LMAN2 CDK10 FAM105A DUSP6 Module cyan EEF1D LOC728453 ZNF33B PTDSS1 PMM1 TULP4 ARL1 CSDA WDR40A LOC731985 TRIM58 SSNA1 SF4 RPS29 ADIPOR1 SNCA ERCC5 GALT LOC100132499 LOC653635 LOC440359 ANKRD54 LOC130773 PDZK1IP1 LOC441775 MRPL40 LOC100130255 WDR70 MARCH8 VIL2 Module turquoise LOC346887 C9orf72 LAX1 IGFBP4 C3orf26 NOTCH2 RGS18 NCOA4 TRIB2 MAX BID LOC641710 CDS2 MRPS9 B4GALT5 FAM193B DSE LOC388707 SLAMF6 IRAK3 MEF2A PARP1 SNN ARPC5 AUTS2 SNX6 FAM98A C9orf66 HEY1 ALOX5

TABLE 23 Top 30 Genes with the Highest Gene Significance Correlated with Brain Size Variation in Control Module greenyellow CDC2 KIF11 NUSAP1 MELK PRC1 DTL DEPDC1B TTK OIP5 CCNA2 UHRF1 TYMS KIF20A KPNA2 MCM10 UBE2C TK1 CENPE NUF2 ASPM KIAA0101 DLGAP5 CDC20 CCNE2 DONSON EZH2 GMNN MGC40489 NEK2 NCAPG Module grey60 IGLL3 CRKRS CAMK1G PERP HSPA13 SPATS2 IGLL1 SLC25A4 GGH CD38 ELL2 UAP1 MGC29506 BIK LOC401845 PRDX4 TNFRSF17 XBP1 SEC61B GLDC LOC649210 LOC652694 LOC652493 FKBP11 IGJ CAV1 TXNDC5 LOC649923 LOC647506 LOC652102 Module midnightblue SMOX ARHGAP18 SPARC HIST1H2AG C15orf26 PLOD2 C16orf68 ARHGAP21 TREML1 XPNPEP1 ANKRD9 TAL1 C5orf62 C11orf59 KIFC3 LOC650261 LOC441481 ESAM TSPAN9 GP9 GNG11 GRB14 CMTM5 ITGA2B CLDN5 CALD1 PF4V1 LY6G6F TUBA4A GPX1 Module yellow ZNF426 ELMOD2 ILKAP LOC644739 PRDM1 PDPK1 LOC653344 TGFBR2 UPF2 ZNF480 DMAP1 CCDC28B VARS FAM44A NTHL1 KLHDC4 MYO9A OTUD1 C10orf118 IPMK TCP11L2 PHF3 BTBD2 PHF20L1 PCSK7 STRA13 PDE4B KIF22 RTN4 TMEM106C Module cyan SNORD8 ZNF33A AKAP7 C20orf108 BLVRB UBE2F DERL2 PPIG EWSR1 SF4 HPS1 C17orf68 HEMGN DSCAM TESC LOC100134108 NDUFAF1 LOC100134102 LOC100130769 HECTD3 GSPT1 MAPK13 KRT1 SRRD SNF8 PPP2R2A IGF2BP2 LOC652968 RN5S9 PDZK1IP1 Module turquoise PPARBP PPOX ZNF551 ZNF135 ACOT4 MSTO1 CEP290 MPZL1 CPPED1 KIAA1641 METT11D1 NUP43 BTBD6 OPTN METTL2A USP36 TMEM45B TOP3B XYLT2 ZNF805 ALG9 TBK1 IRAK1BP1 DIS3L EFHC2 TMEM217 MGC42367 LRRC25 IL8RB DCAF7

TABLE 24 Top 30 Genes with the Highest Module Membership Correlated with Brain Size Variation in ASD Module greenyellow DLGAP5 CDKN3 HMMR OIP5 KIAA0101 CEP55 NUSAP1 KIF11 BUB1 TOP2A ASPM CCNA2 CCNB2 TYMS CHEK1 NCAPG PTTG3P CDC45L AURKA MELK MCM10 CDC2 CENPE GMNN UBE2C PRC1 PTTG1 CDCA5 MND1 TTK Module grey60 TXNDC5 ABCB9 TNFRSF17 MGC29506 FKBP11 CD38 CRKRS SEC11C LOC647506 CAMK1G LOC647450 LOC652694 CAV1 LOC652493 GGH DNAJB11 FAM46C ITM2C ELL2 GLDC IGLL1 IGJ ARMET LOC390712 LOC642131 HSP90B1 SLC25A4 LOC642113 IGLL3 LOC651751 Module midnightblue SH3BGRL2 GP9 CSTDPL PDE5A TUBB1 ESAM ITGB5 SEPT5 TREML1 PTGS1 CTTN PTCRA MYLK NRGN MARCH2 SELP ALOX12 TSPAN9 SDPR ACRBP ABLIM3 PEAR1 DDEF2 F13A1 ITGA2B GNG11 ASAP2 CMTM5 DNM3 C7orf41 Module yellow NDUFS3 POMGNT1 LOC729279 CPSF4 DGCR6 MRPS12 AIP POLR3C PAFAH1B3 KRTCAP2 MRPL37 ADSL L3MBTL2 BMS1 NUDT1 IMP4 RPUSD2 VEGFB LAGE3 WDR54 C19orf53 LAT C11orf2 EIF3B B4GALT3 APRT DHPS TRAPPC6A NDUFS8 C17orf70 Module cyan LOC642469 SPRYD3 LOC440313 SERPINA13 HBD EPB42 LOC100131726 DPYSL5 AHSP SLC25A39 GPR175 MUC6 SELENBP1 ROPN1B LOC100131164 IFIT1L LOC100131391 STRADB RNF213 FBXO7 HBQ1 UBXN6 EPB49 ALAS2 TESC SESN3 SEMA6B WDR40A HBE1 TMEM111 Module turquoise ITPRIP REPS2 SEPX1 STX3 AQP9 FCGR2A NFIL3 NUMB LOC730278 PSG3 BASP1 TLR1 RNF149 NCF4 LOC100134728 RALB PHC2 LIMK2 TLR8 GK PSG9 SLC22A4 CCPG1 CEACAM3 FTHL12 FAM49A KCNJ2 GCA FPR1 LOC729009

TABLE 25 Top 30 Genes with the Highest Module Membership Correlated with Brain Size Variation in Control Module greenyellow C12orf48 HMMR NCAPG CDKN3 DLGAP5 CCNB2 CDC20 CDC45L TOP2A CHEK1 PTTG3P NUSAP1 CEP55 PTTG1 MND1 CDC2 BUB1 DEPDC1B NEK2 KIAA0101 KIF11 AURKA GMNN OIP5 TYMS ASPM CCNE2 NUF2 CCNA2 CDCA5 Module grey60 MGC29506 TNFRSF17 TXNDC5 ABCB9 LOC647450 LOC652694 LOC652493 LOC642113 GLDC LOC647506 CD38 IGJ SEC11C IGLL1 CRKRS FKBP11 CAV1 BUB1 ARMET CAMK1G FAM46C GGH ITM2C LOC390712 IGLL3 DNAJB11 SPATS2 HSP90B1 XBP1 ELL2 Module midnightblue ITGB5 GP9 PDE5A TSPAN9 SDPR TUBB1 CTTN ITGB3 PTCRA NRGN PPBP PTGS1 SEPT5 NCKAP5 CTDSPL ESAM ALOX12 SH3BGRL2 TREML1 F13A1 ACRBP C5orf4 GP1BA ELOVL7 ITGA2B GNG11 DDEF2 PROS1 TNFSF4 ANKRD9 Module yellow CPSF4 NDUFS3 DNAJC8 LOC391811 ITPA PAFAH1B3 KRTCAP2 ADSL NDUFS8 WDR54 DCTPP1 SAE1 NDUFB8 NUDT1 SCAMP3 CUTA C19orf48 CCT7 NHP2L1 NHP2 PDXP PTPRCAP LSM2 MRPS12 ATIC TTC4 CCT3 NXT1 IMP3 DPH2 Module cyan LOC642469 GPR175 AHSP LOC100131726 LOC440313 SPRYD3 MUC6 HBD SLC25A39 EPB49 EPB42 STRADB LOC389599 FBXO7 UBXN6 DPYSL5 LOC100131164 SERPINA13 SELENBP1 LOC100131391 RNF213 HBE1 TRIM58 MYL4 SNCA SEMA6B CSDA LOC440359 ROPN1B HBQ1 Module turquoise GCA PFKFB4 SRGN TLR6 NUMB SEPX1 TLR1 FTHL12 ACSL1 LIMK2 MNDA S100A11 NFIL3 ITPRIP RALB LIN7A TLR8 STX3 LILRB3 PSG9 FCGR2A GK LOC730278 FTHL7 PHC2 REPS2 PGCP FPR1 RNF149 LOC729009 Discussion

In this naturalistic study of autism brain size and gene expression conducted during very early development, evidence of specific early functional genomic pathology related to brain development and size in vivo in ASD toddlers was identified. Results show abnormal brain development and size in ASD toddlers involves disruption of cell cycle and protein folding networks plus induction of abnormal functioning of cell adhesion, translation and immune gene networks. Also, dysregulation of DNA-damage, cell cycle regulation, apoptosis, mitogenic signaling, cell differentiation and immune system response gene networks was replicated in both ASD study groups. It was previously reported several of these gene networks are disrupted in prefrontal cortex in postmortem ASD children². Thus, postmortem and the present in vivo evidence raise the theory that very early, probably prenatal, disruption of several key developmental gene networks leads to known defects of abnormal neuron number⁶, brain^(6-9,11,12) and body²⁷ growth, and synaptic development and function²⁸, as previously reported.^(7,11,29-31)

In the brain in animal model studies,^(32,33) cell cycle and protein folding networks impact cerebral cortical neuron production and synapse development, respectively, and, therefore brain and cortical size and function. Using a novel approach that combines MRI and gene expression, it was discovered that gene expression signals of both networks are detectable in the blood in control toddlers and, remarkably, are strongly correlated with brain and cerebral size, including cortical surface area. Variations in brain size in ASD toddlers are only weakly correlated with cell production and protein folding expression levels, and instead are more strongly related to a variety of other functions, namely cell adhesion, immune/inflammation, translation and other developmental processes. Thus, even given similar brain sizes or cortical surface areas in ASD versus control toddlers, the genetic foundations for brain development and growth are apparently distinctly different. Dysfunction of cell cycle processes has long been theorized to underlie brain growth pathology in ASD⁷. The present evidence along with recent evidence of a 67% overabundance of prefrontal cortical neurons in ASD boys⁶ underscores the relevance of this theory to elucidating the molecular and cellular developmental neuropathology and origins of ASD.

Dysregulation of cell adhesion networks, as well as protein folding in ASD toddlers, likely point to underlying abnormalities of synapse development and function, as well as to global alterations of transcriptional regulation.^(34,35) Accumulation of misfolded proteins leads to the Unfolded Protein Response (UPR)³⁶. Converging evidence shows that misfolded proteins and UPR may underlie impaired synaptic function in autism³⁷, as well as in neurodegenerative disorders³⁸. Moreover, results of modeling studies of neurexin and neuroligin mutations identified in autistic patients, show ER retention and point to UPR as a mechanism behind synaptic malfunction in autism^(34,39,40). Due to preponderance of highly penetrant mutations, the disruption of synaptic cell adhesion molecules is a well-established mechanism underlying ASD pathophysiologyl⁴, and recent evidence extends implications to dysregulation at the network level²⁸. The instant findings show that genes of the integrin family are abnormally “activated” in ASD, and thus may underlie aberrant synaptic structure and function⁴¹ as well as affect regulation of apoptosis, proliferation, migration and cell differentiation. Integrins also play roles in modulation of microglia behavior, and thereby additionally participate in regulation of neural inflammation and immune response⁴¹.

Immune gene networks were dysregulated in both ASD study groups and were among top networks correlated with brain size in ASD, but not TD, toddlers. Dysregulation of immune/neuroinflammation mechanisms is a strong signal in a large number of studies of older ASD children and adults.^(26,42) The present study, however, is the first to find significant dysregulation of immune/neuroinflammation gene networks at about the age of first clinical risk signs of ASD and the first to show a relationship with ASD brain development. Recently, abnormal immune/neuroinflammation gene expression in frozen cortex tissue has been reported in two independent studies of young as well as older postmortem autism cases.^(2,28) Microglial activation, which typically occurs in association with neuroinflammation, was reported in prefrontal cortex across all ages studied from 2 years to adulthood in ASD.^(43,44) While evidence of immune involvement has been argued to be a secondary later abnormality in ASD, there is no experimental evidence to favor that idea over the possibility that ASD involves both prenatal immune alterations as demonstrated by studies modeling prenatal maternal immune activation (MIA) in rodents⁴⁵. Abnormal cell cycle control and cortical cell number strongly point to prenatal origins, and whether and how they and other genetic dysregulation and pathological cellular events intersect with immune alterations deserves careful investigation. In either event, this study provides the first evidence that immune gene networks are dysregulated at the age of first clinical concern and referral at 1 to 2 years of age and already relate to ASD brain development.

This study is unique in that it identified a candidate genomic signature that has a high level of accuracy, specificity and sensitivity in diagnostic classification of Discovery ASD vs control (TD and contrast) toddlers all of whom came from a general, naturalistic population screening. The strategy, which used the 1-Year Well-Baby Check-Up Approach, allowed the unbiased, prospective recruitment and study of ASD and control toddlers as they occur in the community pediatric clinics, something not previously done by research groups. Thus, not only did the ASD toddlers reflect the wide clinical phenotypic range expected in community clinics but the control toddlers also reflect the natural mix of typically developing, mild language delayed, transient language delayed, and global developmental delayed toddlers commonly seen in community clinics. Against this challenging control group, the signature of this study surprisingly correctly identified 82.5% of Discovery ASD toddlers. The candidate signature from this discovery sample performed well in the independent replication cohort, despite the completely different version of microarray chip used with that cohort.

This very good level of accuracy outperforms other behavioral and genetic screens for ASD infants and toddlers reported in the literature, especially when compared with performance of other tests applied to the young general pediatric population (as opposed to preselected syndromic patients or ASD patients from multiplex families). For example, the M-CHAT, a commonly used parent report screen, has very low specificity (27%)⁴⁶ and positive predictive value (PPV, 11-54%) when used in general populations^(47,48). While important strides have been made in understanding possible genetic risk factors in autism³, current DNA tests detect only rare autism cases and lack specificity⁴⁹ or confirm autism at older ages and have not been demonstrated to be effective in ASD infants and toddlers²⁶. Thus, the candidate functional genomic signature reported here, developed from a general pediatric population, is currently the best performing blood- or behavior-based candidate classifier in ASD infants and toddlers.

The results of this study support the model that in a great majority of affected toddlers, ASD involves disruption of a comment set of key neural developmental genetic pathways. These commonly disrupted pathways govern neuron number and survival, neuronal functional integrity and synapse formation, which are key neural developmental processes. Disruption of immune genetic networks is also involved in the majority ASD toddlers, an effect not detected in DNA studies of gene mutations and CNVs, but one that is found in ASD prefrontal brain tissue. Evidence indicates it is no longer a question of whether immune disruption is involved in ASD, but rather why and how. A subset of genes in these common pathways—notably translation, immune/inflammation, cell adhesion and cell cycle genes—provide a candidate genomic signature of risk for autism at young ages. Knowledge of these common pathways can facilitate research into biological targets for biotherapeutic intervention and development of accurate biomarkers for detecting risk for ASD in infants in the general pediatric population.

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Example 2 Additional Methods, Analyses, and Results

Subjective Recruitment, Tracking and Developmental Evaluation

All toddlers were developmentally evaluated by a Ph.D. level psychologist and those that were younger than 3 years at the time of blood draw were tracked every 6 months until their 3^(rd) birthday when a final diagnosis was given. Only toddlers with a provisional or confirmed ASD diagnosis were included in this study. Toddlers were recruited via the 1-Year Well-Baby Check-Up Approach, a new general population based screening approach designed to identify toddlers with an ASD around the 1^(st) birthday or from general community sources (e.g., referred by a friend, or response to the website). In brief, the 1-Year Well-Baby Check-Up Approach utilizes a broad band screening tool, the CSBS DP IT Checklist) implemented at the routine first year pediatric exam. The recent study, which included the participation of 137 pediatricians who implemented >10,000 CSBS screens, showed that 75% of toddlers that fail the screen at the 1^(st) year exam have a true delay (either ASD, language delay, global developmental delay or other condition). While ASD toddlers were as young as 12 months at the time of blood sampling, all but 3 toddlers have been tracked and diagnosed using the ADOS toddler module³ until at least age two years, an age where diagnosis of ASD is relatively stable⁴⁻⁶. Toddlers received the ADOS module that was most appropriate for their age and intellectual capacity. For the Discovery sample 64% of ASD population had an ADOST, 31% had an ADOS 1, and 5% had an ADOS 2 while for the replication sample 32% of ASD population had an ADOS T, 48% had an ADOS 1 and 20% had an ADOS 2. Only toddlers with a provisional or confirmed ASD diagnosis were included in this study. Twenty-four final diagnoses for participants older than 30 months were also confirmed with the Autism Diagnostic Interview—Revised³.

All toddlers participated in a battery of standardized and experimental tests that included the Autism Diagnostic Observation Schedule³, the Mullen Scales of Early Learning′ and the Vineland Adaptive Behavior Scales⁸. Diagnoses were determined via these assessments and the Diagnostic and Statistical Manual, Fourth Edition (DSM IV-TR)⁹. Testing sessions generally lasted 4 hours and occurred across 2 separate days and the blood sample was usually taken at the end of the first day. All standardized assessments were administered by experienced Ph.D. level psychologists.

Ethnicity or Race information was self-reported by parents. Discovery subjects: ASD (87 subjects) were 44 Caucasian, 24 Hispanic, 13 Mixed, 4 Asian, 1 Indian, 1 African-American, ethnicity; control (55 subjects) were, 38 Caucasian, 7 Hispanic, 5 mixed, 2 African American, 3 Asian ethnicity. Replication subjects: ASD (44 subjects) were 23 Caucasian, 13 Hispanic, 6 mixed, 2 Asian ethnicity; control (29 subjects) were 20 Caucasian, 4 Hispanic/Latino, 3 mixed, 1 African American ethnicity, 1 unreported.

In order to monitor health status, the temperature of each toddler was taken using an ear digital thermometer immediately preceding the blood draw. If temperature was higher than 99, then the blood draw was rescheduled for a different day. Parents were also asked questions regarding their child's health status such as the presence of a cold or flu, and if any illnesses were present or suspected, the blood draw was rescheduled for a different day.

RNA Extraction, Preparation and Quality Control

Four-to-six ml of blood was collected into EDTA-coated tubes from toddlers on visits when they had no fever, cold, flu, infections or other illnesses or use of medications for illnesses 72 hours prior blood-draw. Blood samples were passed over a LEUKOLOCK filter (Ambion, Austin, Tex., USA) to capture and stabilize leukocytes and immediately placed in a −20° (C.) freezer.

Total RNA was extracted following standard procedures and manufacturer's instructions (Ambion, Austin, Tex., USA). In principle, LEUKOLOCK disks were freed from RNA-later and Tri-reagent was used to flush out the captured lymphocyte and lyse the cells. RNA was subsequently precipitated with ethanol and purified though washing and cartridge-based steps. The quality of mRNA samples was quantified by the RNA Integrity Number (RIN) and values of 7.0 or greater were considered acceptable¹⁰ all processed RNA samples passed RIN quality control. Quantification of RNA was performed using Nanodrop (Thermo Scientific, Wilmington, Del., USA). Samples were prep in 96-well plates at the concentration of 25 ng/uL.

MRI Scanning and Neuroanatomic Measurement

A T1-weighted IR-FSPGR sagittal protocol (TE=2.8 ms, TR=6.5 ms, flip angle=12 deg, bandwidth=31.25 kHz, FOV=24× cm, slice thickness=1.2 mm, 165 images) was collected during natural sleep¹¹.

FSL's linear registration tool (FLIRT) rigidly registered brain images to a custom template that was previously registered into MNI space¹². Registered images were then processed through FSL's brain extraction tool (BET) removing skull and non-brain tissue¹³. Remaining non-brain tissue was removed by an anatomist to ensure accurate surface measurement. Gray matter, white matter and CSF were segmented via a modified version of the FAST algorithm¹⁴ using partial volumes rather than neighboring voxels to increase sensitivity for detecting thin white matter in the developing brain¹⁵. The brain was divided into cerebral hemispheres, cerebellar hemispheres, and brainstem via Adaptive Disconnection¹⁶. Each cerebral hemisphere mask was subtracted from a sulcal mask generated by BrainVisa and recombined with the original FSL segmentation to remove all sulcal CSF voxels. The final hemisphere mask was reconstructed into a smoothed, 3-dimensional mesh in BrainVisa to obtain surface measures¹⁷.

Gene Expression and Data Processing

RNA was assayed at Scripps Genomic Medicine (La Jolla, Calif., USA) for labeling, hybridization, and scanning using expression BeadChips pipeline (Illumina, San Diego, Calif., USA) per the manufacturer's instruction. All arrays were scanned with the Illumina BEADARRAY READER and read into Illumina GENOMESTUDIO software (version 1.1.1). Raw data was exported from Illumina GENOMESTUDIO and data pre-processing was performed using the lumi package¹⁸ for R (R-project.org) and Bioconductor (bioconductor.org)¹⁹.

Several quality criteria were used to exclude low quality arrays as previously described.^(20,21) In brief, low-quality arrays were those with poor signal intensity (raw intensity box plots and average signal >2 standard deviations below the mean), deviant pair-wise correlation plots, deviant cumulative distribution function plots, deviant multi-dimensional scaling plots, or poor hierarchical clustering²². Five samples (four ASD and one Control) were identified as low quality due to poor detection rates, different distributions and curved dot plots, and were removed prior normalization. Eighteen (18) samples had 1 replicate and all pair-wise plots of each replica had a correlation coefficient of 0.99. Hierarchical clustering of these replicated samples showed 13 samples having with the two replicas that clustered together, therefore the B array was arbitrarily chosen for the following steps. For the remaining 5 of these replicated samples, the two replicas did not cluster together, thus the averaged gene expression levels were used in the following steps. No batch effects were identified. Raw and normalized data is deposited in Gene Expression Omnibus (GSE42133). BrB-array filtering Tool was used to obtain a final set of genes without missing expression values. Filtering criteria were Log Intensity Variation (P>0.05) and percent missing (>50% of subjects). 142 final samples/arrays (87 ASD, 55 control), and thus 142 unique subject datasets, were deemed high quality and entered the expression analysis. Inter-array correlation (IAC) was 0.983.

Differentially expressed genes (DE; P<0.05) were obtained by class comparison (ASD versus control) in BRB-Array Tool using a random variance model. The DE genes from the discovery toddlers was then used to identify differentially expressed pathways (Metacore) and a potential gene expression signature of ASD. The latter one was then validated on the replication toddlers. Both discovery and replication datasets underwent the same filtering and normalization steps.

WGCNA and Association Analyses

Weighted Gene Correlation Network Analysis (WGCNA) package^(23,24) was used to identify functional associations between gene modules and neuroanatomic measures across all discovery subjects. Co-expression analysis was run by selecting the lowest power for which the scale-free topology fit index reached 0.90 and by constructing a signed (i.e., bidirectional) network with a hybrid dynamic branch cutting method to assign individual genes to modules²⁵. Gene Significance (GS; absolute value of the correlation between gene expression levels and neuroanatomical measure) and Module Membership (MM; measure of intramodular connectivity or co-expression across genes within each biologically relevant module) were also computed using WGCNA. GS versus MM was computed to provide a measure of gene activity patterns change between ASD and control groups (See, labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/) for manuals and further details. To identify gene-brain associations within each study group separately, the WGCNA analyses were also performed within ASD and control groups of the discovery sample.

Hypergeometric and Venn Analyses

Hypergeometric distribution analysis was performed using the function sum(dhyper( )) in R. The total number of human genes from which random gene-sets of equal size were taken to test the significance of the identified gene-sets were: 21,405 for the enrichment analyses (this number represents all genes annotated in the Metacore database), 20,151 for the Venn analyses involving the DE genes and gene modules (this number represents all genes passing the pre-processing analysis of the discovery study) and 26,210 for the Venn analysis of the CNV gene-content (this number represents all refseq human genes currently mapped and present on the Illumina platform HumanHT-12v4). The number of unique genes within autism relevant CNV below 1 Mb in size was 4611 and was obtained from the analysis of the AutDB database (see, mindspec.org/autdb.html). Only cases strictly annotated as ASD with/without additional features (for examples: mental retardation, neurocognitive impairment) were selected. Cases annotated as intellectual disability, developmental delay, language delay Asperger syndrome, broad spectrum autism, bipolar disorder, learning disability even if associated with autistic features, were not selected. Only CNVs from the UCSC build 36 (Human Genome 18) were selected. Venn analysis was performed using the online tool at pangloss.com/seidel/Protocols/venn.cgi.

Classifier and Performance Analysis

Twelve module eigengenes were obtained from the WGCNA analysis of the 2765 DE genes in the discovery sample. Identification of the four modules was based on AUC performance after logistic regression in the same sample. The pair of modules that best performed in distinguishing ASD from control subjects was identified. Next, whether adding each single extra module would increase or decrease performance was tested and if performance increased that module was retained. The four modules (blue, black, purple and greenyellow) displayed the best AUC performance and were used to independently validate the classifier.

To validate the classifier gene-weights were calculated from the genes of the selected modules using their correlation with the eigengene values. Weights were applied to the gene expression levels of each replication subject and eigengenes were computed and used in the logistic regression to independently validate the classification performance. Clinical and MRI characteristics between the correctly classified and misclassified groups (ASD and control) were compared to determine if the classifier was sensitive to these measures. Results for the Mullen, ADOS, and Vineland scores were compared. Residual brain volumes for total brain volume, cerebral white and grey matter, and cerebellar white and grey matter were also compared.

TABLE 26 Pearson and Spearman correlations of module-eigengenes and diagnosis (Dx) MODULE Dx Top Network Green −0.18*/ns  Inflammation_interferon signaling Black 0.24**/0.2{circumflex over ( )} Translation_Translation initiation Magenta  −0.24**/−0.25{circumflex over ( )}{circumflex over ( )} ns Purple  −0.26**/−0.32{circumflex over ( )}{circumflex over ( )}{circumflex over ( )} Cell cycle_Meiosis Salmon −0.39***/−0.4{circumflex over ( )}{circumflex over ( )}{circumflex over ( )}  ns MidnightBlue  0.18*/0.18{circumflex over ( )} Cell adhesion_integrin-mediated LightCyan −0.37***/−0.34{circumflex over ( )}{circumflex over ( )}{circumflex over ( )} ns DarkRed   −0.2*/−0.20{circumflex over ( )} ns Signif. codes: p-value Pearson ***<0.001; **<0.01; *<0.05; p-value Spearman {circumflex over ( )}{circumflex over ( )}{circumflex over ( )}<0.001; {circumflex over ( )}{circumflex over ( )}<0.01; {circumflex over ( )}<0.05; ns = not significant enrichment

REFERENCES

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Example 3 Age-Related Changes in Gene Expression in ASD and Non-ASD Controls

Age-related changes in ASD signature genes from infancy to young childhood were analyzed and compared to non-ASD controls. We discovered several patterns of age-dependent expression changes across ASD signature genes, including but not limited to the following three examples: First, genes were identified that showed main effects of diagnosis (ASD vs Control) and no statistically significant age-related changes (FIG. 13A; ASD—light grey vs Control—dark grey). For these genes (which are in the minority of all ASD signature genes), absolute expression level predicted diagnostic classification regardless of age at testing. Second, other genes were identified that showed main effects of diagnosis plus main effects of age (FIG. 13B); these represented a large portion of all ASD signature genes. Thus, for these genes knowledge of absolute expression level could give erroneous classification unless age at testing was taken into account. Third, still other signature genes were identified that showed an interaction between age and diagnosis (FIG. 13C) such that at some ages expression levels were greater in ASD than control, while at other ages expression levels did not significantly differ between ASD and control and at still other ages expression levels in controls exceeded ASD. A large portion of signature fell into this category of age-related change in gene expression level in ASD and controls. The age at which ASD and control expression change trajectories intersected varied across genes with some intersecting at early ages, others at 2-3 years and others after 2 to 3 years of age. For these genes, knowledge of absolute expression level will give completely erroneous classification unless age at testing is computationally taken into account.

Knowledge we developed of these age-dependent changes in the expression levels of each and every signature gene is incorporated into the WGERD and is computationally combined with the weighted-gene expression values so that, with age changes as a predictor for each gene, we have optimized age-specific signatures of ASD. Given the child's age at the time of bioassay and the expression levels of each gene, the program calculates age-adjusted weight-gene expression values for the child to compare to the WGERD age-adjusted weight gene expression signature. Using different numbers of signature genes (ie, 10, 20, 40, 80, 160, etc) age-adjusted expression signatures out-perform expression signatures without any age correction by 4% to 10%. See FIG. 14 for one example of the invention's performance enhancement when knowledge of age effects is combined with gene expression (FIG. 14a ) versus when age-adjusted calculations per gene are not used (FIG. 14b ) as well as Table 3 above.

Example 4 Weighted Gene Expression Values in Combination with GeoPref Test Score

The magnitude of the problem articulated above in the Background section is substantial and immediate: Given the current prevalence rates, every year 52,000 and 84,000 babies born will go on to develop ASD. Therefore, there is an immediate need for feasible, practical, cost-effective and clinical-effective biological ASD tests that reduce the age of accurate and specific detection, evaluation, and referral to as young an age as possible in real world community settings. Procedures that have poor ASD specificity worsen the problem. Procedures that lack sensitivity leave a huge number of babies under-detect and un-diagnosed, which also fails to address the magnitude of the problem. Tests that are expensive, such as whole genome sequencing fail to address the problem because they are so expensive.

In brief, prior methods have not delivered screening, detection and diagnostic evaluation approaches that are easy, quick, and cost-effective to implement in ordinary community settings anywhere and by staff ordinarily present in the clinics. Missing from these methods is high ASD-specificity and very good sensitivity so that a large portion of all true cases of babies, 1 to 2 year olds, 2 to 3 year olds, and 3 to 4 year olds with ASD are detected and correctly diagnosed and a minimum percentage of non-ASD babies are not falsely misdiagnosed as ASD.

The methods of the invention provide the first procedure with a surprisingly high level of specificity and very good sensitivity in an easy, quick and cost-effective way. In some embodiments, the invention does this by using a novel method that combines gene expression as described above and GeoPref test data and signatures in the MMSM.

The GeoPref Test is fully described in Pierce et al. (2011). In brief, the GeoPref Test is a simple and quick 1-minute eye-tracking test that can be administered as a screen or evaluation test to individuals in the general pediatric population. Babies, infants, toddlers and young children are shown a computer screen that displays colorful moving patterns on one side (the “Geo” side) and lively moving children on the other (the “Social” side). Eye-tracking and scoring of how much time a child looks at one side or the other is automated. A child that looks at the “Geo” side by more than a threshold amount of time during a 1-minute test is considered a Geo preference (or “GeoPref”) responder. GeoPref responders among babies, infants, toddlers and young children have a 99% chance of being ASD but only 20 to 30% of all ASD cases are detected by this test.

By computationally combining the weighted gene expression values and GeoPref score of a child, a gene expression-GeoPref signature of the child is obtained, and comparing it to the MMSM reference database compute a score for that child's ASD risk is computed based on divergence of the child's GeoPref MMSM signature to the GeoPref MMSM reference database. In one embodiment of this procedure, accuracy remains at 85% and sensitivity drops slightly to 72%, but ASD-specificity is a 98%. This is the highest overall performance of any previous biological or biobehavioral ASD test applied at any age from birth to 4 years. Importantly, this combined WGSM/MMSM signature is capable of very high beneficial impact in screening and diagnostic evaluation because it not only detects a very large portion of the general pediatric ASD population at young ages via a simple, quick 1 minute test plus ordinary blood draw to get a gene expression bioassay, but it has an extremely high correct detection rate and a very low false positive rate. Thus, it addresses in a very meaningful way the need for early and correct detection and diagnostic determination of ASD among the 52,000 to 84,000 babies born every year in the US who do develop ASD.

REFERENCES

-   1. Pierce, K., Conant, D., Hazin, R., Stoner, R. & Desmond, J.     Preference for geometric patterns early in life as a risk factor for     autism. Archives of General Psychiatry 68, 101-9 (2011).

Example 5 Weighted Gene Expression Values in Combination with Protein Signatures of ASD

ASD and other diseases are manifested by changes in gene expression, metabolite profiles and in the expression, post-translational processing and protein and small molecule interactions among the cellular and non-cellular constituents of blood and other tissues. There is wide variation in the correlation between gene expression and the level of any particular protein or modified variant thereof. These variations in the levels of particular proteins and protein variants have been found to correlate with disease and disease progression in numerous examples. Additionally, the poor correlation between gene expression and patterns of relative abundance of protein variants suggests that production of protein variants is subject to different aspects of disease biology than is gene expression, and further suggests that measurement of patterns of protein variants in blood and other tissues could be a valuable adjunct assessment of disease in combination with weighted gene expression.

Therefore, in certain embodiments, MMSM includes, but is not limited to, assays of proteins in peripheral blood. As with RNA tests, only a subset of blood proteins are likely to change in ways that allow their measurement to be informative for diagnosing autism. Simple changes in the abundance of certain proteins may be correlated with ASD, and measurement of the concentration of one or more of these proteins either directly in blood or extracted from blood can have diagnostic value. Useful measurement techniques span a range of specificity and technical approaches. Highly specific measurement of proteins derived from specific unique genes can use antibody reagents to specifically quantify particular protein species. The same approach can be extended to analyze large numbers of different proteins using collections of antibodies targeting the detection of multiple different protein species to enable the measurement of the abundance of larger groups of proteins in blood. For diagnostic assays, each of the antibodies would be chosen to recognize species of proteins that vary in abundance or protein quality as a function of ASD status, and this relationship to ASD would be established by experiment. Analogous to the weighted gene signatures used in the development of our diagnostic RNA signature, measurement of the weighted expression signature of multiple proteins can also be used to combine the ASD related changes in these proteins into a molecular fingerprint of ASD. An extension of this approach to use simple abundance measurements as a weighted diagnostic signature is to find and use ASD-associated changes in other protein properties to use as diagnostic molecular signatures. In addition to abundance measurement, these other informative changes include changes in protein post-translational modification, protein three dimensional conformation, complex formation with other serum components (other protein or non-protein components of blood) and changes in the ability to interact with ligands (e.g. protein or small molecules that can bind the proteins changed by ASD). Assays to discover these ASD changes in protein abundance or properties can also be incorporated directly or indirectly into diagnostic assays.

Protein signatures of ASD can be discovered by a large number of combinations of fractionation and analysis techniques. Whole blood proteins may be directly analyzed (for example using ForteBio Octet or other immunodetection systems), or the cellular and non-cellular fractions can be separated and separately analyzed with variable levels of fractionation of both cellular and plasma fractions. In general, analysis of proteins within a fraction becomes easier as the fraction is reduced in complexity by fractionation, but some analytical techniques can work directly on unfractionated or less fractionated samples. There is a long history of development of new protein extraction and fractionation techniques applied to research and commercial fractionation, purification and analysis of proteins to answer research questions or produce protein products. In general, proteins can be fractionated by solubility (e.g. by ammonium sulfate fractional precipitation, or by partitioning between solvents of differing composition), by selecting for particular binding affinity for functionalized surfaces (e.g. selecting for protein fractions with differing affinity for ion exchange or reverse phase matrices in HPLC, or for other more specific affinity reagents such as antibodies coupled to solid phase substrates or small molecule derivatized surfaces) or by selecting for specific migration characteristics in sieving matrices (e.g. size exclusion chromatography or electrophoresis). The affinity reagents used to capture and quantify specific protein species can be general (binding to all variants of a protein product of a particular gene), or the reagents could be specific for particular variants derived by post-translational processing, conformational change or liganding (e.g. antibodies specific for post-translationally modified forms of a protein). Once separated, the proteins can be analyzed by a number of techniques to identify and quantify particular proteins. Those skilled in the art would use mass spectrometry to define the genetic identity and quantity of intact or fragmented proteins within a mixture, or would use antibody or other specific affinity reagents to quantify these proteins.

As an example, we explored for protein biomarkers of ASD by doing immunoassays for the following 9 biomarkers: TNF-α, IL-6, IL-10, IP-10, sIL-6R, sFas, VEGF, sVEGFR-1 and tPAI-1 in serum samples derived from the following collection of 142 pediatric patients presenting for clinical assessment of ASD status.

Language All Typical Delayed (LD) ASD 142 66 27 49

The results of this analyses suggested that abnormalities in levels of sFas (elevated) and, VEGF, sIL-6R, and IL-6 (all reduced) are significantly associated with ASD relative to TD patients. This demonstrates that there are multiple protein biomarkers of ASD, and integration of measurements of these protein changes into combination tests for ASD (e.g. combining weighted gene expression signatures, behavioral tests and measurements of blood protein composition) is expected to enhance the overall test performance. Extending this discovery approach to larger and more complex patient sets and to the use of additional combinations of fractionation, detection and protein identification will expand this list of diagnostically relevant protein changes, and choosing which tests to incorporate into combined assays is determined by prospective clinical trials as with the initial discovery of the weighted gene expression signatures. These results are a proof of principle demonstration that serum expression levels of proteins and protein variants can change as a function of ASD status, and that measurement of these levels can therefore be used as additional diagnostic assays in conjunction with WGSM in MMSM.

Other embodiments and uses are apparent to one skilled in the art in light of the present disclosures. Those skilled in the art will appreciate that numerous changes and modifications can be made to the embodiments of the invention and that such changes and modifications can be made without departing from the spirit of the invention. It is, therefore, intended that the appended claims cover all such equivalent variations as fall within the true spirit and scope of the invention. 

We claim:
 1. A method of conducting a weighted gene and feature test of autism (WGFTA) for autism screening, diagnosis or prognosis, comprising: a) measuring gene expression levels for a subject for a group of genes consisting of at least 80 or more genes, wherein at least 20 or more genes are selected from each of the four gene sets listed in Tables 1.1 through 1.4 with absolute weight values ranging from about 0.50 to about 1.00 to form a set of raw gene expression data; b) normalizing the gene expression level for each gene in the set of raw gene expression data to form a set of normalized gene expression data; c) determining weighted gene expression levels for each gene in the set of normalized gene expression data using gene-specific weights from a reference autism weighted gene signature matrix (WGSM) to generate a set of weighted gene expression data, wherein the gene-specific weights are adjusted based on an age of the subject; and d) determining for the subject a risk, diagnosis, or prognosis of autism by comparing a divergence of the set of weighted gene expression data to reference gene expression data from the reference autism WGSM.
 2. The method of claim 1, wherein the reference autism weighted gene signature matrix (WGSM) is derived from gene expression data from at least 40 healthy individuals and 40 autistic individuals.
 3. The method of claim 1, wherein the at least 20 or more genes are involved in cell cycle, protein folding, cell adhesion, translation, DNA damage response, apoptosis, immune/inflammation functions, signal transduction ESR1-nuclear pathway, transcription-mRNA processing, cell cycle meiosis, cell cycle G2-M, cell cycle mitosis, cytoskeleton-spindle microtubule, and cytoskeleton-cytoplasmic microtubule functions.
 4. The method of claim 1, wherein the determining for the subject the risk, diagnosis, or prognosis of autism further comprises comparing a divergence of a gene-network signature matrix (GNSM) of the subject to a reference autism GNSM wherein each said GNSM comprises interaction patterns of specific gene-weights and features calculated from gene-to-gene interactions, and wherein said interaction patterns are calculated based on the relationship or state of a gene with non-genomic features.
 5. The method of claim 1, wherein the determining for the subject the risk, diagnosis, or prognosis of autism further comprises comparing a divergence of a multi-modal signature matrix (MMSM) of the subject to a reference autism MMSM wherein each said MMSM contains a quantification of non-genomic features obtained by clinical, behavioral, anatomical, and functional measurements.
 6. The method of claim 5, wherein said non-genomic features comprise age, a GeoPreference test, a MRI test, a fMRI test, a DTI test, an Autism Diagnostic Observation Schedule (ADOS) test, or a Communication and Symbolic Behavior Scales (CSBS) test.
 7. The method of claim 6, wherein said non-genomic feature is age.
 8. The method of claim 1, wherein the determining for the subject the risk, diagnosis, or prognosis of autism further comprises comparing a divergence of a collateral feature signature matrix (CFSM) of the subject to a reference autism CFSM, wherein each said CFSM comprises analytes in maternal blood during pregnancy, a sibling with autism, or maternal genomic signature or preconditions.
 9. The method of claim 1, wherein the group of genes consists of at least 160 or more genes, wherein at least 40 or more genes are selected from each of the four gene sets listed in Tables 1.1 through 1.4.
 10. The method of claim 1, wherein the group of genes consists of up to 200 genes, wherein 50 genes are selected from each of the four gene sets listed in Tables 1.1 through 1.4. 